what is text analysis in qualitative research

Compendium for Early Career Researchers in Mathematics Education pp 181–197 Cite as

Qualitative Text Analysis: A Systematic Approach

• Udo Kuckartz 4  
• Open Access
• First Online: 27 April 2019

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Part of the book series: ICME-13 Monographs ((ICME13Mo))

Thematic analysis, often called Qualitative Content Analysis (QCA) in Europe, is one of the most commonly used methods for analyzing qualitative data. This paper presents the basics of this systematic method of qualitative data analysis, highlights its key characteristics, and describes a typical workflow. The aim is to present the main characteristics and to give a simple example of the process so that readers can assess whether this method might be useful for their own research. Special attention is paid to the formation of categories, since all scholars agree that categories are at the heart of the method.

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1 Introduction: Qualitative and Quantitative Data

Thematic analysis, often called Qualitative Content Analysis (QCA) in Europe, is one of the most commonly used methods for analyzing qualitative data (Guest et al. 2012 ; Kuckartz 2014 ; Mayring 2014 , 2015 ; Schreier 2012 ). This chapter presents the basics of this systematic method of qualitative data analysis, highlights its key characteristics, and describes a typical workflow.

Working with codes and categories is a proven method in qualitative research. QCA is a method that is reliable, easy to learn, transparent, and it is a method that is easily understood by other researchers. In short, it is a method that enjoys a high level of recognition and is to be highly recommended, especially in the context of dissertations.

The aim of this paper is to present the main characteristics and to give a simple example of the process so that readers can assess whether this method might be useful for their own research. Special attention is paid to the formation of categories, since all scholars agree that categories are at the heart of the method.

Let’s start with some of the basics of data analysis in empirical research: What does ‘qualitative data’ mean, and what do we mean by ‘quantitative data’? Quantitative data entail numerical information that results, for example, from the collection of data from a standardized interview. In a quantitative data matrix, each row corresponds to a case, namely, an interview with a respondent. The columns of the matrix are formed by the variables. Table  8.1 therefore shows the data of four cases, here the respondents 1–4. Six variables were collected for these individuals, on a scale of 1–6, concerning how often they perform certain household activities (laundry, small repairs etc.). Typically, these kinds of data sets are available in social research in the form of a rectangular matrix, for instance as shown in Table  8.1 .

A matrix like this that consists of numbers can be analyzed using statistical methods. For example, you can calculate univariate statistics such as mean values, variance, and standard deviations. You can also generate graphical displays such as box plots or bar charts. In addition, variables can be related to each other, for example by using methods of correlation and regression statistics. Another form of analysis tests groups for differences. In the above study, for example, the questions ‘Are women more frequently engaged in laundry than men in the household?’ and ‘Are men more frequently engaged in minor repairs than women in the household?’ can be calculated using an analysis of variance.

Qualitative data are far more diverse and complex than quantitative data. These data may comprise transcripts of face-to-face interviews or focus group discussions, documents, Twitter tweets, YouTube comments, or videos of the teacher-student interactions in the classroom.

In this chapter, I restrict the presentation of the QCA method to a specific type of data, namely qualitative interviews. This collective term can be used to describe very different forms of interviews, such as guideline-assisted interviews or narrative interviews on critical life events conducted in the context of biographical research. The latter can last several hours and comprise more than 30 pages as a transcription. A qualitative interview may also consist of a short online survey, like the one I conducted in preparation for my workshop at the International Congress on Mathematical Education (ICME-13).

Obviously, the different types of qualitative data are not as easy to analyze as the numbers in a quantitative data matrix. Numerous analytical methods have been developed in qualitative research, among them the well-proven method of qualitative content analysis.

2 Key Points of Qualitative Content Analysis

What are the key points of the qualitative content analysis method? Regardless of which variant of QCA is used, the focus will always be on working with categories (codes) and developing a category system (coding frame). What Berelson formulated in 1952 for quantitative content analysis still applies today, both to quantitative and qualitative content analysis:

Content analysis stands or falls by its categories (…) since the categories contain the substance of the investigation, a content analysis can be no better than its system of categories. (Berelson 1952 , p. 147)

Categories are therefore of crucial importance for effective research, not only in their role as analysis tools, but also insofar as they form the substance of the research and the building blocks of the theory the researchers want to develop. That raises the question ‘What are categories?’—or more precisely, ‘What are categories in the context of empirical social research?’ Answering this question is by no means easy and there are at least two ways of doing so. The first way can be described as phenomenological : Kuckartz ( 2016 , pp. 31–39) focuses on the use of this term in the practice of empirical social research, i.e., drawing attention to what is called a category in empirical social research. The result of this analysis is a very diverse spectrum, whereby several different types of categories can be distinguished in social science research literature (ibid., pp. 34–35):

Factual categories denote actual or supposed objective circumstances such as ‘length of training’ or ‘occupation’.

Thematic categories refer to certain topics, arguments, schools of thought etc. such as ‘inclusion’, ‘environmental justice’ or ‘Ukrainian conflict’.

Evaluative categories are related to an evaluation scale—usually ordinal types, for example the category ‘helper syndrome’ with the characteristics ‘not pronounced’, ‘somewhat pronounced’ and ‘pronounced’. For evaluative categories, it is the researchers who classify the data according to predefined criteria.

Analytical categories are the result of intensive analysis of the data, i.e., these categories move away from the description of the data, for example by means of thematic categories.

Theoretical categories are subspecies of analytical categories that refer to an existing theory, such as Ajzen’s theory of planned behavior, Ainsworth’s attachment theory, or Foucault’s analysis of power.

Natural categories , also called “in vivo codes” (Charmaz 2006 , p. 56; Kuckartz 2014 , p. 23), are terms used by the actors in the field.

Formal categories denote formal characteristics of an analysis unit, e.g., the length of time in an interview.

The above list is not complete; there are many more types of categories and corresponding methods of coding (Saldana 2015 ).

A second way of answering the question ‘What is a category?’ can be described as conceptual and historical; this way leads us far back into the history of philosophy. The conceptual historical view of the term, originating from ancient Greece, starts with Greek philosophy more than 2000 years ago. Plato and Aristotle already dealt with categories—Aristotle even in an elaboration of the same term (“categories”). The study of categories runs through Western philosophy from Plato and Kant to Peirce and analytical philosophy. The philosophers are by no means in agreement on the concept of categories, but a discussion of the differences between the different schools would far exceed the scope of this paper; Instead, reading the mostly very extensive contributions on the terms ‘category’ and ‘category theory’ in the various lexicons of philosophy is recommended. Categories are basic concepts of cognition; they are—generally speaking—a commonality between certain things: a term, a heading, a label that designates something similar under certain aspects. Categories also play this role in content analysis, as the following quote from the Content Analysis textbook of Früh ( 2004 ) demonstrates:

The pragmatic sense of any content analysis is ultimately to reduce complexity from a certain research-led perspective. Text sets are described in a classifying manner with regard to characteristics of theoretical interest. In this reduction of complexity, information is necessarily lost: On the one hand, information is lost due to the suppression of message characteristics that are present in the examined texts but are not of interest in connection with the present research question; on the other hand, information is lost due to the classification of the analyzed message characteristics. According to specified criteria, some of them are each considered similar to one another and assigned to a certain characteristic class or a characteristic type, which is called ‘category’ in the content analysis. The original differences in meaning of the message characteristics uniformly grouped in a category shall not be taken into account. (p. 42, translated by the author)

But how does qualitative content analysis arrive at its categories, the basic building blocks for forming theory? There are three principal ways to develop categories:

Concept-driven (‘deductive’) development of categories; in this case the categories

are derived from a theory or

derived from the literature (the current state of research) or

derived from the research question (e.g. directly related to an interview guide)

Data-driven (‘inductive’) development of categories; the characteristics here are

the step-by-step procedure,

the method of open coding until saturation occurs,

the continuous organization and systematization of the formed codes, and

the development of top-level codes and subcodes at different levels.

Mixing a concept-driven and data-driven development of codes:

The starting point here is usually a coding frame with deductively formed codes and

the subsequent inductive coding of all data coded with a specific main category.

The terms deductive and inductive are often used for the concept-driven and data-driven approaches, respectively. However, the use of the term ‘deductive’ is rather problematic in this context: In scientific logic, the term ‘inductive’ refers to the abstract conclusion from what has been observed empirically to a general rule or a law; this has little to do with the formation of categories based on empirical data. The situation is similar with the term ‘deductive’: In scientific logic, the deductive conclusion is a logical consequence of its premises; the formation of categories based on the state of research, a theory, or an advanced hypothesis is very different. Categories do not necessarily emerge from a systematic literature review or from a research question. Due to its skid resistance, however, the word pair ‘inductive-deductive’ will probably remain in the language theorem of empirical social research or the formation of categories for a long time to come. Nevertheless, I try to avoid the terms inductive and deductive, and—like Schreier ( 2012 , p. 84)—prefer the terms ‘data-driven’ and ‘concept-driven’ for these different approaches to the formation of categories.

The decisive action in QCA is the coding of the data, i.e. a precisely defined part of the material is selected, and a category is assigned. As shown in the following figure, this may be a passage from an interview. Here, paragraph 15 of the text was coded with the code Simultaneousness (Fig.  8.1 ).

Text passage with a coded text segment

The individuals who perform this segmentation and coding of the data are referred to as coders. In this context, we also speak of “inter- and intracoder agreement” (reliability) (Krippendorff 2012 ; Kuckartz 2016 ; Schreier 2012 ). In quantitative content analysis, the units to be coded are usually defined in advance and referred to as coding units. In qualitative content analysis, on the other hand, coding units are not usually defined in advance; they are created by the coding process.

The general workflow of a qualitative content analysis is in Fig.  8.2 . In all variants the research question plays the central role in this method: It provides the perspective for the textual work necessary at the beginning, that is, the intensive reading and study of the texts (Kuckartz 2016 , p. 45). For qualitative methods, it is common for the individual analysis phases to be carried out on a circular basis. This also applies to QCA: The creation of categories and subcategories and the coding of the data can take place in several cycles. Saldana ( 2015 ) speaks of first cycle coding and second cycle coding, for example. The number of cycles is not fixed, and only in rare cases would one get by with just a single cycle.

The five phases of qualitative content analysis

Once all the data have been coded with the final category frame, a systematization and structuring of all the relevant data in view of the research questions at hand will have been achieved. Table  8.2 illustrates a model of such a thematic matrix. It is similar to the quantitative data matrix shown in Fig.  8.2 , but instead of containing numbers, the cells of the matrix now contain text excerpts coded with the respective corresponding category.

The further analysis of the matrix can now take two directions: If you look at columns, you can examine certain topics. These forms of analysis can be described as ‘category-based’. Looking at the rows, you can focus on cases (people) and carry out a ‘case-oriented analysis’.

Category-based analyses can focus on a specific category or even consider several categories simultaneously. For example, the statements made by the research participants can be contrasted between two or across several topics. Such complex analyses can lead to very rich descriptions or to the determination of influencing factors and effects, which can then be displayed in a concept map. Case-oriented analyses allow you to identify similarities between cases, identify extreme cases, and form types. Methods of consistently comparing and contrasting cases can be used to this end. For example, if you have determined a typology, you can then visualize it as a constellation of clusters and cases.

3 The Analysis Process in Detail

The example used in the following is a short online survey conducted in preparation for the ‘Workshop on qualitative text analysis’ as part of the ICME 13. The aim of the survey was to provide an overview of the research needs of the participants and their level of knowledge. In other words, its aim was descriptive and not about the development of hypotheses or a theory. In this online interview, I asked the following five questions and asked the participants to write their responses directly below the questions. Table  8.3 contains the resulting qualitative data.

Typically, QCA consists of six steps

Step 1: Preparing the data, initiating text work

Step 2: Forming main categories corresponding to the questions asked in the interview

Step 3: Coding data with the main categories

Step 4: Compiling text passages of the main categories and forming subcategories inductively on the material; assigning text passages to subcategories

Step 5: Category-based analyses and presenting results

Step 6: Reporting and documentation.

Since the purpose of the survey in this case was to get an overview of the relevant interests of the workshop participants and to tailor the workshop to their needs, the last step was omitted. There was no need for reporting and documentation.

The first phase consists of preparing of the data and conducting an initial read-through the responses; the analysis of this short survey did not require extensive interpretation of the responses. Since respondents used different fonts and font sizes in their e-mails, these had to be standardized first when preparing the data. In addition, the overall formatting was also adjusted to render it more uniform across responses. This would not have been absolutely necessary for the analysis, but without this preparation, later compilations of coded text passages might have looked rather chaotic.

In the second phase of QCA, categories are formed. When analyzing data obtained through an online survey, it is best to create a set of main categories based on the questions asked. In this analysis, the following five categories were formed for the first coding cycle:

Motives and goals

Experience with QCA

Specific questions about QCA

Experience with QDAS ( Q ualitative d ata a nalysis s oftware)

Academic discipline.

Since the questions in the online survey were numbered, the numbers were retained for better orientation, but they could have been dispensed with without any problems.

According to the differentiation of categories laid out earlier in this paper, the categories Motives and goals and Specific questions about QCA are thematic categories. Category 5 Academic Discipline is a factual code. The other two categories Experience with QCA and Experience with QDAS are about the experiences with the method and with QDA software. If the researcher is interested in the extent of participants’ experience, both categories are evaluative categories; alternatively, if the specific type of experience is the primary point of interest, the categories are thematic. Since the aim of this survey was to get an overview of the level of knowledge and practical experience of the respondents, an overview was sufficient; detailed knowledge of the types of experience the participants had gained was not absolutely necessary. Reading the responses also demonstrated that the respondents understood the question in this sense and that in most cases no specific details were provided. In any case, working with software like MAXQDA guarantees that you can always return to the original texts should this be useful or necessary during the course of the analysis.

In the third phase of the analysis, the corresponding text segments are coded with the five main categories. Figure  8.3 shows a screenshot of the software MAXQDA after this first cycle of coding was performed on the survey responses. The assignments of the codes are displayed to the left of the corresponding text sections.

Display of a text with code assignments after the first cycle of coding

In the following fourth phase of the analysis, the coding frame is developed further. To do this, all the text passages coded with one of the main categories are first compiled, a procedure which is also referred to as retrieval . Subcodes are then developed directly in the relation to this data—in other words, the creation of categories is data-driven. This process is described in the following with regard to the first main category Motives and goals :

The category Motives and goals coded the responses to the question regarding what the participants wanted to learn in the workshop. First, all text passages to which this category was assigned were compiled. Then each of these text passages was coded a second time. This was done with a procedure similar to that of open coding in Grounded Theory (Strauss and Corbin 1990 ). In this case, the codes were short sequences of words that described what the participants wanted to learn:

analyze mathematics textbook curricula

learn type-building analysis

analyze e-portfolios and group discussions

analyze responses to open-ended questions

learn more about different research methods

how to establish credibility in practice

learn more about rigor within the process and how to ensure its validity

the role of reliability coefficients

insight into conducting qualitative research

learn about the QCA method

how to code video transcripts

how to take the richness of data into account (not only numbers)

analyze large numbers of open questions

learn more about a few different approaches to choose from

searching for a suitable method to analyze the interviews

interesting for me to see how colleagues are working.

As part of the software MAXQDA there is a module called “Creative Coding” that allows you to visually group codes obtained through the open coding method. After arranging the open codes, seven subcategories were created for the category “Motives and Objectives”, namely

Getting an overview of qualitative research

Getting an overview of QCA

Learning basic techniques

Learning about special type of analysis

Reliability and validity

Learning to analyze special types of data

Interesting for me to see how colleagues are working.

Figure  8.4 shows a visual display of the category formation; the original statements are assigned to the respective category. It turns out that many participants in the workshop were mainly interested in obtaining an overview of qualitative content analysis and qualitative research in general. The graph also implicitly illustrates the differences between a quantitative and qualitative analysis of the responses: Four participants (a comparatively large proportion) wanted to learn how to analyze specific types of data, but a closer look at the details, that is, the qualitative dimension, reveals that the types of data the respondents had in mind were completely different.

Visualization of the motives grouped into subcategories

Once the subcategories have been created, all the data coded with the main category Motives and goals must be coded a second time. This is also known as the second coding cycle. In this sample survey, all the coded text passages were included in the formation of the subcategories due to the relatively small sample. In the case of small sample sizes like this, the Creative Coding module automatically reassigns the subcategories. In the case of larger samples, however, category formation will usually be carried out only with a subsample and not with all the data, or the process of open coding will be performed only until the system of subcategories appears saturated and no further subcategories need to be redefined. Then, of course, the data that have not been considered up to this point must still be coded in line with the final category system.

The two categories Experience with QCA and Experience with QDAS were used to code the text passages in which the respondents reported on their experience with the QCA method and the use of QDA software. For the purposes of preparing the workshop as described above, the analysis should address only whether participants had prior experience and how extensive this experience was. An evaluative category with the values ‘yes’, ‘partial’, ‘no’ was therefore defined.

For the third main category, Specific questions about QCA , no subcategories were formed, since the questions formulated by the participants had to be retained in their wording to answer them in the workshop. However, the questions asked were sorted by topic, and essentially identical questions were summarized.

For category 5, Academic discipline , subcategories were initially formed according to the disciplines mentioned by the respondents. However, it quickly transpired that almost all participants came from the field of mathematics education and that there were only a few individual cases from other fields such as development psychology or primary school teacher (see Fig.  8.5 ). These individual cases were combined into the subcategory others for the final category system, so that ultimately only two subcategories were formed.

Main category “Academic discipline and status” with subcategories

After the main categories have been processed in this way—five in the case of this survey—the fifth phase ‘Category-based analyses and presenting results’ can begin. However, it should be clear that in the fourth phase of the development of the category system, an extensive amount of analytical work has already being carried out. The identification of the different motive types represents an analytical achievement in itself and is, at the same time, the foundation of the corresponding category-based analysis in phase 5. The category Motives and goals was of central importance in this survey. In addition to identifying the various motives, both quantitative and qualitative analyses can now be carried out. Quantitatively, we can determine how many people expressed which motives in their statement. Of course, it is quite possible for someone to have expressed several motives. In terms of a qualitative analysis, we can ask what is behind these categories in greater detail. In relation to the subcategory Learning to analyze special types of data , for example, we could ask which special data types the respondents had in mind here.

The category-based analysis always offers the option of focusing on qualitative and/or quantitative aspects. A frequency analysis of the category Experience with QDAS shows that the vast majority of participants have not yet had any practical experience with QDA software (see Fig.  8.6 ).

Bar chart of the category “Experiences with QDA software”

The question concerning their experience with text analysis methods presents a somewhat different picture. Quantitatively, we can see that more people are experienced in this regard, while the more detailed qualitative view reveals that this experience mainly involved the Grounded Theory method. It is interesting to compare the two categories that deal with experience. Table  8.4 contains an excerpt from such a comparison between five people.

There are also many further possibilities regarding the analysis of interrelationships that can be carried out in this fifth phase. For example, the connection between motives and goals, and previous knowledge and experience, can be examined. In relation to the specific questions asked by respondents in the survey, one could create a cross table (or “crosstab”) in which the questions asked by the experienced group are compared with the questions asked by those with no experience.

There are many other analysis options for larger studies than those presented for the small online survey. Qualitative content analysis is not a method that is always applied in the same way regardless of the data or research questions at hand. Although it is a systematic procedure, it nonetheless offers a flexibility that allows you to adapt it to the respective requirements of a project. There are other analytical possibilities in this regard, which were not mentioned in the above description. Among these, two should be highlighted in particular, namely, the possibility of paraphrasing text passages and the possibility of creating thematic summaries.

Paraphrasing passages of text can be understood in its everyday sense, namely, that researchers reformulate these text passages in their own words. This can be a very useful tool for category development. This technique is especially recommended for beginners, as it forces them to read the text line by line, interpret it to gain a thorough understanding, and then record it in their own words. It is certainly too time-consuming in most cases to edit all texts in this way but paraphrasing a selected subset of texts can sharpen your analytical view and be a valuable intermediate step in the development of a meaningful category system. Moreover, these paraphrases can then be sorted, particularly significant paraphrases can be combined, and gradually more abstract and theoretically rich categories can be formed.

In contrast to paraphrasing texts, formulating thematic summaries assumes that the texts have already been coded. In this approach, all the text passages coded in regard to a specific topic are read for each case and a thematic summary is written for each person. Usually, there is a huge gap between a category and the amount of original text assigned to it in the case of longer qualitative interviews, such as narrative interviews. On the one hand there is a relatively short code, such as ‘Environmental behavior in relation to nutrition’, and on the other there are numerous passages of varying length in which a respondent says something on this subject. A thematic summary summarizes all these passages as said by a certain person from the perspective of the research question. This means that the text is not repeated, but rather edited conceptually. Summaries thus create a second level between the original text and the categories and concepts. They also enable complex analyses to be carried out in which several categories are compared or the statements of different groups (women/men, different age groups, different schooling, etc.) are contrasted. This would be nigh impossible if the original quotations were always used since the amount of text would simply be too large, and it would consequently not be possible to create case overviews. A thematic summary, on the other hand, compresses what one person has said in such a way that it can easily be included in further analyses.

A third possibility the QCA method offers is the visualization of relationships between categories. Diagrams, in the form of concept maps, can be generated in which the influencing factors, effects, and relations are visualized.

Phase 6, ‘Reporting and documentation’, is about putting the results of your analyses on paper. The research report of a project working with the QCA method is usually divided into a descriptive and an analytical section. Depending on the method and the significance of the categories, category-based analyses will be the center of attention. The case dimension, however, which is all too often neglected, should also be taken into account in the report. It is often very valuable for the recipients of the research not only to learn something about the connections between the categories, but also something about the participants, that is, the cases that are consciously selected for such a presentation. It is particularly interesting if the cases are grouped into types and the report presents cases that are representative of these types.

The category-based presentation should be illustrated with quotes from the original material. However, you should also be aware of the danger of selective plausibility, i.e., that one mainly selects quotations that clarify the alleged connections between categories, while contradictory examples are not considered. For this reason, counterexamples should always be sought and included in the report.

Category-based analysis should not be limited to a description of the results per category but should also look at the relationships between two or more categories. In other words, you should move from the initial description to the development of a theory.

4 Summary and Conclusions

This chapter presents a method for the methodically controlled analysis of texts in empirical research. To conclude, therefore, the characteristics of the QCA method are concisely summarized:

The focus of the QCA method is on the categories with which the data are coded.

The categories of the final coding frame are described as precisely as possible and it is ensured that the coding procedure itself is reliable, i.e., that different coders concur in their coding.

The data must be coded completely. Complete in this sense means that all passages in the texts that are relevant to the research question are coded. It does, however, make sense to leave those parts of the data uncoded, which are outside the focus of the research question.

The codes and categories can be formed in different ways: empirically, i.e., based directly on the material, or conceptually, i.e., based on the current state of research or on a theory/hypothesis or, rather, as an implementation of the guidelines used in an interview or focus group.

The QCA method is carried out in several phases, ranging from data preparation, category building and coding—which may run in several cycles—to analysis, report writing and presenting the results. QCA therefore means more than just coding the data. Coding is an important step in the analysis, but it is ultimately a preparation for the subsequent analytical steps.

The actual analysis phase consists of summarizing the data, and constantly comparing and contrasting the data. The analysis techniques can be qualitative as well as quantitative. The qualitative analysis may, for example, consist of comparing the statements of certain groups (for instance according to their characteristics, e.g., socio-demographic characteristics) on certain topics. Differences and similarities are identified and summarized in a report. Quantitative analyses may, on the other hand, consist of comparing the frequency of certain categories and/or subcategories for certain groups.

Summary tables and diagrams (e.g., concept maps) can play an important role in the analysis. A good example of a presentation in table form would be a case overview of selected research participants (or groups), in which their statements on certain topics, their judgements and variable values are displayed. An example of a concept map would be a diagram of the determined causal effects of different categories.

Visualizations can also have a diagnostic function in QCA—similarly to imaging procedures in medicine. For example, a ‘cases by categories’ or ‘categories by categories’ display can help identify patterns in the data and indicate which categories are particularly frequently or particularly rarely associated with certain other categories.

When analyzing texts, you should keep in mind that you are working in the field of interpretation. It can be assumed that texts or statements could be interpreted differently. Instead of adopting a constructivist ‘anything goes’ approach, the QCA method tries to reach a consensus—as far as this is possible—on the subjective meaning of statements and tries to define the categories formed or used by it so precisely that an intersubjective agreement can be achieved in the application of the categories.

Group processes play an important role in this process of achieving the necessary level of agreement. Divergent assignments to categories are discussed as a team and should result in an improvement of the category definitions. Categories for which no agreement can be reached in the coding of relevant points in the data must be excluded from the analysis. Content analysis stands and falls by its categories. An analysis with the help of categories that are interpreted and applied differently in the research team, does not make sense.

QCA does not claim to be the best method but recognizes that it has its limits (the interpretation barrier) and that its results have to face comparison with those of competing methods.

The systematic approach of QCA is multidisciplinary and can be applied in many disciplines, including mathematics education (Schwarz 2015 ). This method is particularly appropriate when working with clearly formulated research questions, because these questions play the central role in this method. Indeed, in every phase of the analysis there is a strong reference to the questions leading the research. One strength of QCA is that it can be used both to describe social phenomena and to develop theories or test hypotheses (Hopf 2016 , pp. 155–166).

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Kuckartz, U. (2019). Qualitative Text Analysis: A Systematic Approach. In: Kaiser, G., Presmeg, N. (eds) Compendium for Early Career Researchers in Mathematics Education . ICME-13 Monographs. Springer, Cham. https://doi.org/10.1007/978-3-030-15636-7_8

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The Practical Guide to Textual Analysis

• Getting Started
• How Does It Work?
• Use Cases & Applications

Textual analysis is the process of gathering and examining qualitative data to understand what it’s about.

But making sense of qualitative information is a major challenge. Whether analyzing data in business or performing academic research, manually reading, analyzing, and tagging text is no longer effective – it’s time-consuming, results are often inaccurate, and the process far from scalable.

Fortunately, developments in the sub-fields of Artificial Intelligence (AI) like machine learning and natural language processing (NLP) are creating unprecedented opportunities to process and analyze large collections of text data.

Thanks to algorithms trained with machine learning it is possible to perform a myriad of tasks that involve analyzing text, like topic classification (automatically tagging texts by topic), feature extraction (identifying specific characteristics in a text) and sentiment analysis (recognizing the emotions that underlie a given text).

Below, we’ll dive into textual analysis with machine learning, what it is and how it works, and reveal its most important applications in business and academic research:

Getting started with textual analysis

• What is textual analysis?
• Difference between textual analysis and content analysis?
• What is computer-assisted textual analysis?
• Methods and techniques
• Why is it important?

How does textual analysis work?

• Text classification
• Text extraction

Use cases and applications

• Customer service
• Customer feedback
• Academic research

Let’s start with the basics!

Getting Started With Textual Analysis

What is textual analysis.

While similar to text analysis , textual analysis is mainly used in academic research to analyze content related to media and communication studies, popular culture, sociology, and philosophy.

In this case, the purpose of textual analysis is to understand the cultural and ideological aspects that underlie a text and how they are connected with the particular context in which the text has been produced. In short, textual analysis consists of describing the characteristics of a text and making interpretations to answer specific questions.

One of the challenges of textual analysis resides in how to turn complex, large-scale data into manageable information. Computer-assisted textual analysis can be instrumental at this point, as it allows you to perform certain tasks automatically (without having to read all the data) and makes it simple to observe patterns and get unexpected insights. For example, you could perform automated textual analysis on a large set of data and easily tag all the information according to a series of previously defined categories. You could also use it to extract specific pieces of data, like names, countries, emails, or any other features.

Companies are using computer-assisted textual analysis to make sense of unstructured business data , and find relevant insights that lead to data-driven decisions. It’s being used to automate everyday tasks like ticket tagging and routing, improving productivity, and saving valuable time.

Difference Between Textual Analysis and Content Analysis?

When we talk about textual analysis we refer to a data-gathering process for analyzing text data. This qualitative methodology examines the structure, content, and meaning of a text, and how it relates to the historical and cultural context in which it was produced. To do so, textual analysis combines knowledge from different disciplines, like linguistics and semiotics.

Content analysis can be considered a subcategory of textual analysis, which intends to systematically analyze text, by coding the elements of the text to get quantitative insights. By coding text (that is, establishing different categories for the analysis), content analysis makes it possible to examine large sets of data and make replicable and valid inferences.

Sitting at the intersection between qualitative and quantitative approaches, content analysis has proved to be very useful to study a wide array of text data ― from newspaper articles to social media messages ― within many different fields, that range from academic research to organizational or business studies.

What is Computer-Assisted Textual Analysis?

Computer-assisted textual analysis involves using a software, digital platform, or computational tools to perform tasks related to text analysis automatically.

The developments in machine learning make it possible to create algorithms that can be trained with examples and learn a series of tasks, from identifying topics on a given text to extracting relevant information from an extensive collection of data. Natural Language Processing (NLP), another sub-field of AI, helps machines process unstructured data and transform it into manageable information that’s ready to analyze.

Automated textual analysis enables you to analyze large amounts of data that would require a significant amount of time and resources if done manually. Not only is automated textual analysis fast and straightforward, but it’s also scalable and provides consistent results.

Let’s look at an example. During the US elections 2016, we used MonkeyLearn to analyze millions of tweets referring to Donald Trump and Hillary Clinton . A text classification model allowed us to tag each Tweet into the two predefined categories: Trump and Hillary. The results showed that, on an average day, Donald Trump was getting around 450,000 Twitter mentions while Hillary Clinton was only getting about 250,000. And that was just the tip of the iceberg! What was really interesting was the nuances of those mentions: were they favorable or unfavorable? By performing sentiment analysis , we were able to discover the feelings behind those messages and gain some interesting insights about the polarity of those opinions.

For example, this is how Trump’s Tweets looked like when counted by sentiment:

And this graphic shows the same for Hillary Clinton:

There are many methods and techniques for automated textual analysis. In the following section, we’ll take a closer look at each of them so that you have a better idea of what you can do with computer-assisted textual analysis.

Textual Analysis Methods & Techniques

• Word frequency

Collocation

Concordance, basic methods, word frequency.

Word frequency helps you find the most recurrent terms or expressions within a set of data. Counting the times a word is mentioned in a group of texts can lead you to interesting insights, for example, when analyzing customer feedback responses. If the terms ‘hard to use’ or ‘complex’ often appear in comments about your product, it may indicate you need to make UI/UX adjustments.

By ‘collocation’ we mean a sequence of words that frequently occur together. Collocations are usually bigrams (a pair of words) and trigrams (a combination of three words). ‘Average salary’ , ‘global market’ , ‘close a deal’ , ‘make an appointment’ , ‘attend a meeting’ are examples of collocations related to business.

In textual analysis, identifying collocations is useful to understand the semantic structure of a text. Counting bigrams and trigrams as one word improves the accuracy of the analysis.

Human language is ambiguous: depending on the context, the same word can mean different things. Concordance is used to identify instances in which a word or a series of words appear, to understand its exact meaning. For example, here are a few sentences from product reviews containing the word ‘time’:

Advanced Methods

Text classification.

Text classification is the process of assigning tags or categories to unstructured data based on its content.

When we talk about unstructured data we refer to all sorts of text-based information that is unorganized, and therefore complex to sort and manage. For businesses, unstructured data may include emails, social media posts, chats, online reviews, support tickets, among many others. Text classification ― one of the essential tasks of Natural Language Processing (NLP) ― makes it possible to analyze text in a simple and cost-efficient way, organizing the data according to topic, urgency, sentiment or intent. We’ll take a closer look at each of these applications below:

Topic Analysis consists of assigning predefined tags to an extensive collection of text data, based on its topics or themes. Let’s say you want to analyze a series of product reviews to understand what aspects of your product are being discussed, and a review reads ‘the customer service is very responsive, they are always ready to help’ . This piece of feedback will be tagged under the topic ‘Customer Service’ .

Sentiment Analysis , also known as ‘opinion mining’, is the automated process of understanding the attributes of an opinion, that is, the emotions that underlie a text (e.g. positive, negative, and neutral). Sentiment analysis provides exciting opportunities in all kinds of fields. In business, you can use it to analyze customer feedback, social media posts, emails, support tickets, and chats. For instance, you could analyze support tickets to identify angry customers and solve their issues as a priority. You may also combine topic analysis with sentiment analysis (it is called aspect-based sentiment analysis ) to identify the topics being discussed about your product, and also, how people are reacting towards those topics. For example, take the product review we mentioned earlier for topic analysis: ‘the customer service is very responsive, they are always ready to help’ . This statement would be classified as both Positive and Customer Service .

Language detection : this allows you to classify a text based on its language. It’s particularly useful for routing purposes. For example, if you get a support ticket in Spanish, it could be automatically routed to a Spanish-speaking customer support team.

Intent detection : text classifiers can also be used to recognize the intent of a given text. What is the purpose behind a specific message? This can be helpful if you need to analyze customer support conversations or the results of a sales email campaign. For example, you could analyze email responses and classify your prospects based on their level of interest in your product.

Text Extraction

Text extraction is a textual analysis technique which consists of extracting specific terms or expressions from a collection of text data. Unlike text classification, the result is not a predefined tag but a piece of information that is already present in the text. For example, if you have a large collection of emails to analyze, you could easily pull out specific information such as email addresses, company names or any keyword that you need to retrieve. In some cases, you can combine text classification and text extraction in the same analysis.

The most useful text extraction tasks include:

Named-entity recognition : used to extract the names of companies , people , or organizations from a set of data.

Keyword extraction : allows you to extract the most relevant terms within a text. You can use keyword extraction to index data to be searched, create tags clouds, summarize the content of a text, among many other things.

Feature extraction : used to identify specific characteristics within a text. For example, if you are analyzing a series of product descriptions, you could create customized extractors to retrieve information like brand, model, color, etc .

Why is Textual Analysis Important?

Every day, we create a colossal amount of digital data. In fact, in the last two years alone we generated 90% percent of all the data in the world . That includes social media messages, emails, Google searches, and every other source of online data.

At the same time, books, media libraries, reports, and other types of databases are now available in digital format, providing researchers of all disciplines opportunities that didn’t exist before.

But the problem is that most of this data is unstructured. Since it doesn’t follow any organizational criteria, unstructured text is hard to search, manage, and examine. In this scenario, automated textual analysis tools are essential, as they help make sense of text data and find meaningful insights in a sea of information.

Text analysis enables businesses to go through massive collections of data with minimum human effort, saving precious time and resources, and allowing people to focus on areas where they can add more value. Here are some of the advantages of automated textual analysis:

Scalability

You can analyze as much data as you need in just seconds. Not only will you save valuable time, but you’ll also make your teams much more productive.

Real-time analysis

For businesses, it is key to detect angry customers on time or be warned of a potential PR crisis. By creating customized machine learning models for text analysis, you can easily monitor chats, reviews, social media channels, support tickets and all sorts of crucial data sources in real time, so you’re ready to take action when needed.

Academic researchers, especially in the political science field , may find real-time analysis with machine learning particularly useful to analyze polls, Twitter data, and election results.

Consistent criteria

Routine manual tasks (like tagging incoming tickets or processing customer feedback, for example) often end up being tedious and time-consuming. There are more chances of making mistakes and the criteria applied within team members often turns out to be inconsistent and subjective. Machine learning algorithms, on the other hand, learn from previous examples and always use the same criteria to analyze data .

How does Textual Analysis Work?

Computer-assisted textual analysis makes it easy to analyze large collections of text data and find meaningful information. Thanks to machine learning, it is possible to create models that learn from examples and can be trained to classify or extract relevant data.

But how easy is to get started with textual analysis?

As with most things related to artificial intelligence (AI), automated text analysis is perceived as a complex tool, only accessible to those with programming skills. Fortunately, that’s no longer the case. AI platforms like MonkeyLearn are actually very simple to use and don’t require any previous machine learning expertise. First-time users can try different pre-trained text analysis models right away, and use them for specific purposes even if they don’t have coding skills or have never studied machine learning.

However, if you want to take full advantage of textual analysis and create your own customized models, you should understand how it works.

There are two steps you need to follow before running an automated analysis: data gathering and data preparation. Here, we’ll explain them more in detail:

Data gathering : when we think of a topic we want to analyze, we should first make sure that we can obtain the data we need. Let’s say you want to analyze all the customer support tickets your company has received over a designated period of time. You should be able to export that information from your software and create a CSV or an Excel file. The data can be either internal (that is, data that’s only available to your business, like emails, support tickets, chats, spreadsheets, surveys, databases, etc) or external (like review sites, social media, news outlets or other websites).

Data preparation : before performing automated text analysis it’s necessary to prepare the data that you are going to use. This is done by applying a series of Natural Language Processing (NLP) techniques. Tokenization , parsing , lemmatization , stemming and stopword removal are just a few of them.

Once these steps are complete, you will be all set up for the data analysis itself. In this section, we’ll refer to how the most common textual analysis methods work: text classification and text extraction.

Text classification is the process of assigning tags to a collection of data based on its content.

When done manually, text categorization is a time-consuming task that often leads to mistakes and inaccuracies. By doing this automatically, it is possible to obtain very good results while spending less time and resources. Automatic text classification consists of three main approaches: rule-based, machine learning and hybrid.

Rule-based systems

Rule-based systems follow an ‘if-then’ (condition-action) structure based on linguistic rules. Basically, rules are human-made associations between a linguistic pattern on a text and a predefined tag. These linguistic patterns often refer to morphological, syntactic, lexical, semantic, or phonological aspects.

For instance, this could be a rule to classify a series of laptop descriptions:

( Lenovo | Sony | Hewlett Packard | Apple ) → Brand

In this case, when the text classification model detects any of those words within a text (the ‘if’ portion), it will assign the predefined tag ‘brand’ to them (the ‘then’ portion).

One of the main advantages of rule-based systems is that they are easy to understand by humans. On the downside, creating complex systems is quite tricky, because you need to have good knowledge of linguistics and of the topics present in the text that you want to analyze. Besides, adding new rules can be tough as it requires several tests, making rule-based systems hard to scale.

Machine learning-based systems

Machine learning-based systems are trained to make predictions based on examples. This means that a person needs to provide representative and consistent samples and assign the expected tags manually so that the system learns to make its own predictions from those past observations. The collection of manually tagged data is called training data .

But how does machine learning actually work?

Suppose you are training a machine learning-based classifier. The system needs to transform the training data into something it can understand: in this case, vectors (an array of numbers with encoded data). Vectors contain a set of relevant features from the given text, and use them to learn and make predictions on future data.

One of the most common methods for text vectorization is called bag of words and consists of counting how many times a particular word (from a predetermined list of words) appears in the text you want to analyze.

So, the text is transformed into vectors and fed into a machine learning algorithm along with its expected tags, creating a text classification model:

After being trained, the model can make predictions over unseen data:

Machine learning algorithms

The most common algorithms used in text classification are Naive Bayes family of algorithms (NB), Support Vector Machines (SVM), and deep learning algorithms .

Naive Bayes family of algorithms (NB) is a probabilistic algorithm that uses Bayes’ theorem to calculate the probability of each tag for a given text. It then provides the tag with the highest likelihood of occurrence. This algorithm provides good results as long as the training data is scarce.

Support Vector Machines (SVM) is a machine learning algorithm that divides vectors into two different groups within a three-dimensional space. In one group, you have vectors that belong to a given tag, and in the other group vectors that don’t belong to that tag. Using this algorithm requires more coding skills, but the results are better than the ones with Naive Bayes.

Deep learning algorithms try to emulate the way the human brain thinks. They use millions of training examples and generate very rich representations of texts, leading to much more accurate predictions than other machine learning algorithms. The downside is that they need vast amounts of training data to provide accurate results and require intensive coding.

Hybrid systems

These systems combine rule-based systems and machine learning-based systems to obtain more accurate predictions.

There are different parameters to evaluate the performance of a text classifier: accuracy , precision , recall , and F1 score .

You can measure how your text classifier works by comparing it to a fixed testing set (that is, a group of data that already includes its expected tags) or by using cross-validation, a process that divides your training data into two groups – one used to train the model, and the other used to test the results.

Let’s go into more detail about each of these parameters:

Accuracy : this is the number of correct predictions that the text classifier makes divided by the total number of predictions. However, accuracy alone is not the best parameter to analyze the performance of a text classifier. When the number of examples is imbalanced (for example, a lot of the data belongs to one of the categories) you may experience an accuracy paradox , that is, a model with high accuracy, but one that’s not necessarily able to make accurate predictions for all tags. In this case, it’s better to look at precision and recall, and F1 score.

Precision : this metric indicates the number of correct predictions for a given tag, divided by the total number of correct and incorrect predictions for that tag. In this case, a high precision level indicates there were less false positives. For some tasks ― like sending automated email responses ― you will need text classification models with a high level of precision, that will only deliver an answer when it’s highly likely that the recipient belongs to a given tag.

Recall : it shows the number of correct predictions for a given tag, over the number of predictions that should have been predicted as belonging to that tag. High recall metrics indicate there were less false negatives and, if routing support tickets for example, it means that tickets will be sent to the right teams.

F1 score : this metric considers both precision and recall results, and provides an idea of how well your text classifier is working. It allows you to see how accurate is your model for all the tags you’re using.

Cross-validation

Cross-validation is a method used to measure the accuracy of a text classifier model. It consists of splitting the training dataset into a number of equal-length subsets, in a random way. For instance, let’s imagine you have four subsets and each of them contains 25% of your training data.

All of those subsets except one are used to train the text classifier. Then, the classifier is used to make predictions over the remaining subset. After this, you need to compile all the metrics we mentioned before (accuracy, precision, recall, and F1 score), and start the process all over again, until all the subsets have been used for testing. Finally, all the results are compiled to obtain the average performance of each metric.

Text extraction is the process of identifying specific pieces of text from unstructured data. This is very useful for a variety of purposes, from extracting company names from a Linkedin dataset to pulling out prices on product descriptions.

Text extraction allows to automatically visualize where the relevant terms or expressions are, without needing to read or scan all the text by yourself. And that is particularly relevant when you have massive databases, which would otherwise take ages to analyze manually.

There are different approaches to text extraction. Here, we’ll refer to the most commonly used and reliable:

Regular expressions

Regular expressions are similar to rules for text classification models. They can be defined as a series of characters that define a pattern.

Every time the text extractor detects a coincidence with a pattern, it assigns the corresponding tag.

This approach allows you to create text extractors quickly and with good results, as long as you find the right patterns for the data you want to analyze. However, as it gets more complex, it can be hard to manage and scale.

Conditional Random Fields

Conditional Random Fields (CRF) is a statistical approach used for text extraction with machine learning. It identifies different patterns by assigning a weight to each of the word sequences within a text. CRF’s also allow you to create additional parameters related to the patterns, based on syntactic or semantic information.

This approach creates more complex and richer patterns than regular expressions and can encode a large volume of information. However, if you want to train the text extractor properly, you will need to have in-depth NLP and computing knowledge.

You can use the same performance metrics that we mentioned for text classification (accuracy, precision, recall, and F1 score), although these metrics only consider exact matches as positive results, leaving partial matches aside.

If you want partial matches to be included in the results, you should use a performance metric called ROUGE (Recall-Oriented Understudy for Gisting Evaluation). This group of metrics measures lengths and numbers of sequences to make a match between the source text and the extraction performed by the model.

The parameters used to compare these two texts need to be defined manually. You may define ROUGE-n metrics (n is the length of the units you want to measure) or ROUGE-L metrics (to compare the longest common sentence).

Use Cases and Applications

Automated textual analysis is the process of obtaining meaningful information out of raw data. Considering unstructured data is getting closer to 80% of the existing information in the digital world , it’s easy to understand why this brings outstanding opportunities for businesses, organizations, and academic researchers.

For companies, it is now possible to obtain real-time insights on how their users feel about their products and make better business decisions based on data. Shifting to a data-driven approach is one of the main challenges of businesses today.

Textual analysis has many exciting applications across different areas of a company, like customer service, marketing, product, or sales. By allowing the automation of specific tasks that used to be manual, textual analysis is helping teams become more productive and efficient, and allowing them to focus on areas where they can add real value.

In the academic research field, computer-assisted textual analysis (and mainly, machine learning-based models) are expanding the horizons of investigation, by providing new ways of processing, classifying, and obtaining relevant data.

In this section, we’ll describe the most significant applications related to customer service, customer feedback, and academic research.

Customer Service

It’s not all about having an amazing product or investing a lot of money on advertising. What really tips the balance when it comes to business success is to provide high-quality customer service. Stats claim that 70% of the customer journey is defined by how people feel they are being treated .

So, how can textual analysis help companies deliver a better customer service experience?

Automatically tag support tickets

Every time a customer sends a request, comment, or complain, there’s a new support ticket to be processed. Customer support teams need to categorize every incoming message based on its content, a routine task that can be boring, time-consuming, and inconsistent if done manually.

Textual analysis with machine learning allows you automatically identify the topic of each support ticket and tag it accordingly. How does it work?

• First, a person defines a set of categories and trains a classifier model by applying the appropriate tags to a number of representative samples.
• The model analyzes the words and expressions used in each ticket. For example: ‘I’m having problems when paying with my credit card’ , and it compares it with previous examples.
• Finally, it automatically tags the ticket according to its content. In this case, the ticket would be tagged as Payment Issues .

Automatically route and triage support tickets

Once support tickets are tagged, they need to be routed to the appropriate team in charge to deal with that issue. Machine learning enables teams to send a ticket to the right person in real-time , based on the ticket’s topic, language or complexity. For example, a ticket previously tagged as Payment Issues will be automatically routed to the Billing Area .

Detect the urgency of a ticket

A simple task, like being able to prioritize tickets based on their urgency, can have a substantial positive impact on your customer service. By analyzing the content of each ticket, a textual analysis model can let you assess which of them are more critical and prioritize accordingly . For instance, a ticket containing the words or expressions ‘as soon as possible’ or ‘immediately’ would be automatically classified as Urgent .

Get insights from ticket analytics

The performance of customer service teams is usually measured by KPI’s, like first response time, the average time of resolution, and customer satisfaction (CSAT).

Textual analysis algorithms can be used to analyze the different interactions between customers and the customer service area, like chats, support tickets, emails, and customer satisfaction surveys.

You can use aspect-based sentiment analysis to understand the main topics discussed by your customers and how they are feeling about those topics. For example, you may have a lot of mentions referring to the topic ‘UI/UX’ . But, are all those customers’ opinions positive, negative, or neutral? This type of analysis can provide a more accurate perspective of what they think about your product and get a deeper understanding the overall customer satisfaction.

Customer Feedback

Listening to the Voice of Customer (VoC) is critical to understand the customers’ expectations, experience and opinion about your brand. Two of the most common tools to monitor and examine customer feedback are customer surveys and product reviews.

By analyzing customer feedback data, companies can detect topics for improvement, spot product flaws, get a better understanding of your customer’s needs and measure their level of satisfaction, among many other things.

But how do you process and analyze tons of reviews or thousands of customer surveys? Here are some ideas of how you can use textual analysis algorithms to analyze different kinds of customer feedback:

Analyze NPS Responses

Net Promoter Score (NPS) is the most popular tool to measure customer satisfaction. The first part of the survey involves giving the brand a score from 0 to 10 based on the question: 'How likely is it that you would recommend [brand] to a friend or colleague?' . The results allow you to classify your customers as promoters , passives , and detractors .

Then, there’s a follow-up question, inquiring about the reasons for your previous score. These open-ended responses often provide the most insightful information about your company. At the same time, it’s the most complex data to process. Yes, you could read and tag each of the responses manually, but what if there are thousands of them?

Textual analysis with machine learning enables you to detect the main topics that your customers are referring to, and even extract the most relevant keywords related to those topics . To make the most of your data, you could also perform sentiment analysis and find out if your customers are talking about a given topic positively or negatively.

Analyze Customer Surveys

Besides NPS, textual analysis algorithms can help you analyze all sorts of customer surveys. Using a text classification model to tag your responses can make you save a lot of valuable time and resources while allowing you to obtain consistent results.

Analyze Product Reviews

Product reviews are a significant factor when buying a product. Prospective buyers read at least 10 reviews before feeling they can trust a local business and that’s just one of the (many) reasons why you should keep a close eye on what people are saying about your brand online.

Analyzing product reviews can give you an idea of what people love and hate the most about your product and service. It can provide useful insights and opportunities for improvement. And it can show you what to do to get one step ahead of your competition.

The truth is that going through pages and pages of product reviews is not a very exciting task. Categorizing all those opinions can take teams hours and in the end, it becomes an expensive and unproductive process. That’s why automated textual analysis is a game-changer.

Imagine you want to analyze a set of product reviews from your SaaS company in G2 Crowd. A textual analysis model will allow you to tag g each review based on topic, like Ease of Use , Price , UI/UX , Integrations . You could also run a sentiment analysis to discover how your customers feel about those topics: do they think the price is suitable or too expensive? Do they find it too complex or easy to use?

Thanks to textual analysis algorithms, you can get powerful information to help you make data-driven decisions, and empower your teams to be more productive by reducing manual tasks to a minimum.

Academic Research

What if you were able to sift through tons of papers and journals, and discover data that is relevant to your research in just seconds? Just imagine if you could easily classify years of news articles and extract meaningful keywords from them, or analyze thousands of tweets after a significant political change .

Even though machine learning applications in business and science seem to be more frequent, social science research is also benefiting from ML to perform tasks related to the academic world.

Social science researchers need to deal with vast volumes of unstructured data. Therefore, one of the major opportunities provided by computer-assisted textual analysis is being able to classify data, extract relevant information, or identify different groups in extensive collections of data.

Another application of textual analysis with machine learning is supporting the coding process . Coding is one of the early steps of any qualitative textual analysis. It involves a detailed examination of what you want to analyze to become familiar with the data. When done manually, this task can be very time consuming and often inaccurate or inconsistent. Fortunately, machine learning algorithms (like text classifier models) can help you do this in very little time and allow you to scale up the coding process easily.

Finally, using machine learning algorithms to scan large amounts of papers, databases, and journal articles can lead to new investigation hypotheses .

Final Words

In a world overloaded with data, textual analysis with machine learning is a powerful tool that enables you to make sense of unstructured information and find what’s relevant in just seconds.

With promising use cases across many fields from marketing to social science research, machine learning algorithms are far from being a niche technology only available for a few. Moreover, they are turning into user-friendly applications that are dominated by workers with little or no coding skills.

Thanks to text analysis models, teams are becoming more productive by being released from manual and routine tasks that used to take valuable time from them. At the same time, companies can make better decisions based on valuable, real-time insights obtained from data.

By now, you probably have an idea of what textual analysis is with machine learning and how you can use it to make your everyday tasks more efficient and straightforward. Ready to get started? MonkeyLearn makes it very simple to take your first steps. Just contact us and get a personalized demo from one of our experts!

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Analyzing Text Data

An introduction to text analysis and text mining, an overview of text analysis methods, additional resources.

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What is text analysis.

Text analysis is a broad term that encompasses the examination and interpretation of textual data. It involves various techniques to understand, organize, and derive insights from text, including methods from linguistics, statistics, and machine learning. Text analysis often includes processes like text categorization, sentiment analysis, and entity recognition, to gain valuable insights from textual data.

What is text mining?

Text mining , also known as text data mining, is a process of using computer programs and algorithms to dig through large amounts of text, like books, articles, websites, or social media posts, to find valuable and hidden information. This information could be patterns, trends, insights, or specific pieces of knowledge that are not immediately obvious when you read the texts on your own. Text data mining helps people make sense of vast amounts of text data quickly and efficiently, making it easier to discover useful information and gain new perspectives from written content.

This video is an introduction to text mining and how it can be used in research.

There are many different methods for text analysis, such as:

• word frequency analysis
• natural language processing
• sentiment analysis

These text analysis techniques serve various purposes, from organizing and understanding text data to making predictions, extracting knowledge, and automating tasks.

Before beginning your text analysis project, it is important to specify your goals and then choose the method that will allow you to meet those goals. Then, consider how much data you need, and identify a sampling plan , before beginning data collection.

• Examples of Text and Data Mining Research Using Copyrighted Materials By Sean Flynn and Lokesh Vyas, an exploration of text and data mining across disciplines, from medicine to literature. Published December 5, 2022.
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In This Article Expand or collapse the "in this article" section Textual Analysis and Communication

Introduction, theoretical background.

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• Qualitative Research in Media and Communication Studies

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Textual Analysis and Communication by Elfriede Fürsich LAST REVIEWED: 25 September 2018 LAST MODIFIED: 25 September 2018 DOI: 10.1093/obo/9780199756841-0216

Textual analysis is a qualitative method used to examine content in media and popular culture, such as newspaper articles, television shows, websites, games, videos, and advertising. The method is linked closely to cultural studies. Based on semiotic and interpretive approaches, textual analysis is a type of qualitative analysis that focuses on the underlying ideological and cultural assumptions of a text. In contrast to systematic quantitative content analysis, textual analysis reaches beyond manifest content to understand the prevailing ideologies of a particular historical and cultural moment that make a specific coverage possible. Critical-cultural scholars understand media content and other cultural artifacts as indicators of how realities are constructed and which ideas are accepted as normal. Following the French cultural philosopher Roland Barthes, content is understood as “text,” i.e., not as a fixed entity but as a complex set of discursive strategies that is generated in a special social, political, historic, and cultural context ( Barthes 2013 , cited under Theoretical Background ). Any text can be interpreted in multiple ways; the possibility of multiple meanings within a text is called “polysemy.” The goal of textual analysis is not to find one “true” interpretation—in contrast to traditional hermeneutic approaches to text exegesis—but to explain the variety of possible meanings inscribed in the text. Researchers who use textual analysis do not follow a single established approach but employ a variety of analysis types, such as ideological, genre, narrative, rhetorical, gender, or discourse analysis. Therefore, the term “textual analysis” could also be understood as a collective term for a variety of qualitative, interpretive, and critical content analysis techniques of popular culture artifacts. This method, just as cultural studies itself, draws on an eclectic mix of disciplines, such as anthropology, literary studies, rhetorical criticism, and cultural sociology, along with intellectual traditions, such as semiotics, (post)structuralism, and deconstruction. What distinguishes textual analysis from other forms of qualitative content analysis in the sociological tradition is its critical-cultural focus on power and ideology. Moreover, textual analysts normally do not use linguistic aspects as central evidence (such as in critical discourse analysis), nor do they use a pre-established code book, such as some traditional qualitative content methods. Textual analysis follows an inductive, interpretive approach by finding patterns in the material that lead to “readings” grounded in the back and forth between observation and contextual analysis. Of central interest is the deconstruction of representations (especially but not always of Others with regard to race, class, gender, sexuality, and ability) because these highlight the relationship of media and content to overall ideologies. The method is based in a constructionist framework. For textual analysts, media content does not simply reflect reality; instead, media, popular culture, and society are mutually constituted. Media and popular culture are arenas in which representations and ideas about reality are produced, maintained, and also challenged.

Central to textual analysis is the idea that content as “text” is a coming together of multiple meanings in a specific moment. Barthes 2013 and Barthes 1977 discuss this idea in detail and provide groundbreaking analysis of cultural phenomena in postwar France. Fiske 1987 , Fiske 2010 , and Fiske 2011 provide the standard on how popular culture can be “read,” i.e., interpreted for its ideological assumptions. Because the central aim of textual analysis is to understand how representations are produced in media content, Stuart Hall’s chapter 1 “The Work of Representation” in the renowned textbook Hall, et al. 2013 delivers a compact but comprehensive explanation of representation as a concept. To understand the shift to post-structuralism and concepts, such as discourse, hegemony, and the relationship between language and power, that are central to textual work, one can turn to the works of original theorists such as Foucault 1972 as well as Best and Kellner 1991 for contextualized clarification. Moreover, Deleuze and Guattari 2004 is a foundational post-structural text that radically rethinks the relationship between meaning and practice.

Barthes, Roland. 2013. Mythologies . New York: Hill and Wang.

English translation by Richard Howard and Annette Lavers of the original book published in 1957. Part 1: “Mythologies” consists of a series of short essayistic analyses of cultural phenomena, such as the Blue Guide travel books or advertising for detergents. Part 2: “Myth Today” lays out Barthes’s semiotic-structural approach. Although Barthes later acknowledged the historic contingencies of his interpretations, they remain important as they provided relevant perspective and methodological vocabulary for textual analysis for years to come.

Barthes, Roland. 1977. Image, music, text . Essays selected and translated by Stephen Heath. New York: Hill and Wang.

Classic collection of Barthes’s writing on semiotics and structuralism. For methodological considerations, the chapters “Introduction to the Structural Analysis of Narrative” and “The Death of the Author” are especially relevant.

Best, Steven, and Douglas Kellner. 1991. Postmodern theory: Critical interventions . New York: Guilford.

DOI: 10.1007/978-1-349-21718-2

Accessible introduction to leading postmodern and post-structuralist theorists. For textual analysis, especially the chapter 2 “Foucault and the Critique of Modernity,” chapter 4 “Baudrillard en route to Postmodernity,” and chapter 5 “Lyotard and Postmodern Gaming” provide relevant context for understanding post-structural and postmodern principles.

Deleuze, Gilles, and Félix Guattari. 2004. A thousand plateaus . Translated by Brian Massumi. London and New York: Continuum.

This book is the second part of Deleuze and Guattari’s groundbreaking philosophical project, “Capitalism and Schizophrenia.” Originally published in 1980, it explains central post-structural concepts such as rhizomes, multiplicity, and nomadic thought. Foundational for understanding the production of knowledge and meaning, these ideas have stood the test of time and resonate in networked and digitalized societies in the early 21st century.

Fiske, John. 1987. Television culture . New York: Routledge.

A classic book by Fiske. His “codes of television” (pp. 4–20) explain even for beginning researchers the important relationship among reality, representation, and ideology that is foundational for the textual analysis of any media content even beyond television.

Fiske, John. 2010. Understanding popular culture . 2d ed. London and New York: Routledge.

Important work by Fiske, originally published in 1989, that lays out the theoretical foundations for cultural analysis.

Fiske, John. 2011. Reading the popular . 2d ed. London and New York: Routledge.

Recently reissued companion book to Fiske 2010 . Provides a variety of examples for cultural analysis ranging from Madonna and shopping malls to news and quiz shows.

Foucault, Michel. 1972. The discourse on language. In The archeology of knowledge and the discourse of language . By Michel Foucault, 215–237. Translated by A. M. Sheridan Smith. New York: Pantheon.

Based on the author’s inaugural lecture at the Collège de France in 1970, this appendix provides a fairly succinct introduction to Foucault’s scholarly program and outlines his specific concepts of “discourse.” The author begins to connect discourses to structures of power and knowledge, an argument that becomes more central in his later writings.

Hall, Stuart, Jessica Evans, and Sean Nixon, eds. 2013. Representation: Cultural representations and signifying practices . 2d ed. London and Thousand Oaks, CA: SAGE.

One of the central goals of textual analysis is to understand and interpret media representations. Chapter 1 “The Work of Representation,” by Stuart Hall, is the most comprehensive introduction to this central post-structural cultural studies concept.

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• Textual Analysis | Guide, 3 Approaches & Examples

Textual Analysis | Guide, 3 Approaches & Examples

Published on 7 May 2022 by Jack Caulfield .

Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text – from its literal meaning to the subtext, symbolism, assumptions, and values it reveals.

The methods used to conduct textual analysis depend on the field and the aims of the research. It often aims to connect the text to a broader social, political, cultural, or artistic context.

Table of contents

What is a text, textual analysis in cultural and media studies, textual analysis in the social sciences, textual analysis in literary studies.

The term ‘text’ is broader than it seems. A text can be a piece of writing, such as a book, an email message, or a transcribed conversation. But in this context, a text can also be any object whose meaning and significance you want to interpret in depth: a film, an image, an artifact, even a place.

The methods you use to analyse a text will vary according to the type of object and the purpose of your analysis:

• Analysis of a short story might focus on the imagery, narrative perspective, and structure of the text.
• To analyse a film, not only the dialogue but also the cinematography and use of sound could be relevant to the analysis.
• A building might be analysed in terms of its architectural features and how it is navigated by visitors.
• You could analyse the rules of a game and what kind of behaviour they are designed to encourage in players.

While textual analysis is most commonly applied to written language, bear in mind how broad the term ‘text’ is and how varied the methods involved can be.

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In the fields of cultural studies and media studies, textual analysis is a key component of research. Researchers in these fields take media and cultural objects – for example, music videos, social media content, billboard advertising – and treat them as texts to be analysed.

Usually working within a particular theoretical framework (e.g., postcolonial theory, media theory, semiotics), researchers seek to connect elements of their texts with issues in contemporary politics and culture. They might analyse many different aspects of the text:

• Word choice
• Design elements
• Location of the text
• Target audience
• Relationship with other texts

Textual analysis in this context is usually creative and qualitative in its approach. Researchers seek to illuminate something about the underlying politics or social context of the cultural object they’re investigating.

In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys , as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations.

Textual analysis in the social sciences sometimes takes a more quantitative approach , where the features of texts are measured numerically. For example, a researcher might investigate how often certain words are repeated in social media posts, or which colours appear most prominently in advertisements for products targeted at different demographics.

Some common methods of analysing texts in the social sciences include content analysis , thematic analysis , and discourse analysis .

Textual analysis is the most important method in literary studies. Almost all work in this field involves in-depth analysis of texts – in this context, usually novels, poems, stories, or plays.

Because it deals with literary writing, this type of textual analysis places greater emphasis on the deliberately constructed elements of a text: for example, rhyme and metre in a poem, or narrative perspective in a novel. Researchers aim to understand and explain how these elements contribute to the text’s meaning.

However, literary analysis doesn’t just involve discovering the author’s intended meaning. It often also explores potentially unintended connections between different texts, asks what a text reveals about the context in which it was written, or seeks to analyse a classic text in a new and unexpected way.

Some well-known examples of literary analysis show the variety of approaches that can be taken:

• Eve Kosofky Sedgwick’s book Between Men analyses Victorian literature in light of more contemporary perspectives on gender and sexuality.
• Roland Barthes’ S/Z provides an in-depth structural analysis of a short story by Balzac.
• Harold Bloom’s The Anxiety of Influence applies his own ‘influence theory’ to an analysis of various classic poets.

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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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19 Content Analysis

Lindsay Prior, School of Sociology, Social Policy, and Social Work, Queen's University

• Published: 02 September 2020
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In this chapter, the focus is on ways in which content analysis can be used to investigate and describe interview and textual data. The chapter opens with a contextualization of the method and then proceeds to an examination of the role of content analysis in relation to both quantitative and qualitative modes of social research. Following the introductory sections, four kinds of data are subjected to content analysis. These include data derived from a sample of qualitative interviews ( N = 54), textual data derived from a sample of health policy documents ( N = 6), data derived from a single interview relating to a “case” of traumatic brain injury, and data gathered from fifty-four abstracts of academic papers on the topic of “well-being.” Using a distinctive and somewhat novel style of content analysis that calls on the notion of semantic networks, the chapter shows how the method can be used either independently or in conjunction with other forms of inquiry (including various styles of discourse analysis) to analyze data and also how it can be used to verify and underpin claims that arise from analysis. The chapter ends with an overview of the different ways in which the study of “content”—especially the study of document content—can be positioned in social scientific research projects.

What Is Content Analysis?

In his 1952 text on the subject of content analysis, Bernard Berelson traced the origins of the method to communication research and then listed what he called six distinguishing features of the approach. As one might expect, the six defining features reflect the concerns of social science as taught in the 1950s, an age in which the calls for an “objective,” “systematic,” and “quantitative” approach to the study of communication data were first heard. The reference to the field of “communication” was nothing less than a reflection of a substantive social scientific interest over the previous decades in what was called public opinion and specifically attempts to understand why and how a potential source of critical, rational judgment on political leaders (i.e., the views of the public) could be turned into something to be manipulated by dictators and demagogues. In such a context, it is perhaps not so surprising that in one of the more popular research methods texts of the decade, the terms content analysis and communication analysis are used interchangeably (see Goode & Hatt, 1952 , p. 325).

Academic fashions and interests naturally change with available technology, and these days we are more likely to focus on the individualization of communications through Twitter and the like, rather than of mass newspaper readership or mass radio audiences, yet the prevailing discourse on content analysis has remained much the same as it was in Berleson’s day. Thus, Neuendorf ( 2002 ), for example, continued to define content analysis as “the systematic, objective, quantitative analysis of message characteristics” (p. 1). Clearly, the centrality of communication as a basis for understanding and using content analysis continues to hold, but in this chapter I will try to show that, rather than locate the use of content analysis in disembodied “messages” and distantiated “media,” we would do better to focus on the fact that communication is a building block of social life itself and not merely a system of messages that are transmitted—in whatever form—from sender to receiver. To put that statement in another guise, we must note that communicative action (to use the phraseology of Habermas, 1987 ) rests at the very base of the lifeworld, and one very important way of coming to grips with that world is to study the content of what people say and write in the course of their everyday lives.

My aim is to demonstrate various ways in which content analysis (henceforth CTA) can be used and developed to analyze social scientific data as derived from interviews and documents. It is not my intention to cover the history of CTA or to venture into forms of literary analysis or to demonstrate each and every technique that has ever been deployed by content analysts. (Many of the standard textbooks deal with those kinds of issues much more fully than is possible here. See, for example, Babbie, 2013 ; Berelson, 1952 ; Bryman, 2008 , Krippendorf, 2004 ; Neuendorf, 2002 ; and Weber, 1990 ). Instead, I seek to recontextualize the use of the method in a framework of network thinking and to link the use of CTA to specific problems of data analysis. As will become evident, my exposition of the method is grounded in real-world problems. Those problems are drawn from my own research projects and tend to reflect my academic interests—which are almost entirely related to the analysis of the ways in which people talk and write about aspects of health, illness, and disease. However, lest the reader be deterred from going any further, I should emphasize that the substantive issues that I elect to examine are secondary if not tertiary to my main objective—which is to demonstrate how CTA can be integrated into a range of research designs and add depth and rigor to the analysis of interview and inscription data. To that end, in the next section I aim to clear our path to analysis by dealing with some issues that touch on the general position of CTA in the research armory, especially its location in the schism that has developed between quantitative and qualitative modes of inquiry.

The Methodological Context of Content Analysis

Content analysis is usually associated with the study of inscription contained in published reports, newspapers, adverts, books, web pages, journals, and other forms of documentation. Hence, nearly all of Berelson’s ( 1952 ) illustrations and references to the method relate to the analysis of written records of some kind, and where speech is mentioned, it is almost always in the form of broadcast and published political speeches (such as State of the Union addresses). This association of content analysis with text and documentation is further underlined in modern textbook discussions of the method. Thus, Bryman ( 2008 ), for example, defined CTA as “an approach to the analysis of documents and texts , that seek to quantify content in terms of pre-determined categories” (2008, p. 274, emphasis in original), while Babbie ( 2013 ) stated that CTA is “the study of recorded human communications” (2013, p. 295), and Weber referred to it as a method to make “valid inferences from text” (1990, p. 9). It is clear then that CTA is viewed as a text-based method of analysis, though extensions of the method to other forms of inscriptional material are also referred to in some discussions. Thus, Neuendorf ( 2002 ), for example, rightly referred to analyses of film and television images as legitimate fields for the deployment of CTA and by implication analyses of still—as well as moving—images such as photographs and billboard adverts. Oddly, in the traditional or standard paradigm of CTA, the method is solely used to capture the “message” of a text or speech; it is not used for the analysis of a recipient’s response to or understanding of the message (which is normally accessed via interview data and analyzed in other and often less rigorous ways; see, e.g., Merton, 1968 ). So, in this chapter I suggest that we can take things at least one small step further by using CTA to analyze speech (especially interview data) as well as text.

Standard textbook discussions of CTA usually refer to it as a “nonreactive” or “unobtrusive” method of investigation (see, e.g., Babbie, 2013 , p. 294), and a large part of the reason for that designation is because of its focus on already existing text (i.e., text gathered without intrusion into a research setting). More important, however (and to underline the obvious), CTA is primarily a method of analysis rather than of data collection. Its use, therefore, must be integrated into wider frames of research design that embrace systematic forms of data collection as well as forms of data analysis. Thus, routine strategies for sampling data are often required in designs that call on CTA as a method of analysis. These latter can be built around random sampling methods or even techniques of “theoretical sampling” (Glaser & Strauss, 1967 ) so as to identify a suitable range of materials for CTA. Content analysis can also be linked to styles of ethnographic inquiry and to the use of various purposive or nonrandom sampling techniques. For an example, see Altheide ( 1987 ).

The use of CTA in a research design does not preclude the use of other forms of analysis in the same study, because it is a technique that can be deployed in parallel with other methods or with other methods sequentially. For example, and as I will demonstrate in the following sections, one might use CTA as a preliminary analytical strategy to get a grip on the available data before moving into specific forms of discourse analysis. In this respect, it can be as well to think of using CTA in, say, the frame of a priority/sequence model of research design as described by Morgan ( 1998 ).

As I shall explain, there is a sense in which CTA rests at the base of all forms of qualitative data analysis, yet the paradox is that the analysis of content is usually considered a quantitative (numerically based) method. In terms of the qualitative/quantitative divide, however, it is probably best to think of CTA as a hybrid method, and some writers have in the past argued that it is necessarily so (Kracauer, 1952 ). That was probably easier to do in an age when many recognized the strictly drawn boundaries between qualitative and quantitative styles of research to be inappropriate. Thus, in their widely used text Methods in Social Research , Goode and Hatt ( 1952 ), for example, asserted that “modern research must reject as a false dichotomy the separation between ‘qualitative’ and ‘quantitative’ studies, or between the ‘statistical’ and the ‘non-statistical’ approach” (p. 313). This position was advanced on the grounds that all good research must meet adequate standards of validity and reliability, whatever its style, and the message is well worth preserving. However, there is a more fundamental reason why it is nonsensical to draw a division between the qualitative and the quantitative. It is simply this: All acts of social observation depend on the deployment of qualitative categories—whether gender, class, race, or even age; there is no descriptive category in use in the social sciences that connects to a world of “natural kinds.” In short, all categories are made, and therefore when we seek to count “things” in the world, we are dependent on the existence of socially constructed divisions. How the categories take the shape that they do—how definitions are arrived at, how inclusion and exclusion criteria are decided on, and how taxonomic principles are deployed—constitute interesting research questions in themselves. From our starting point, however, we need only note that “sorting things out” (to use a phrase from Bowker & Star, 1999 ) and acts of “counting”—whether it be of chromosomes or people (Martin & Lynch, 2009 )—are activities that connect to the social world of organized interaction rather than to unsullied observation of the external world.

Some writers deny the strict division between the qualitative and quantitative on grounds of empirical practice rather than of ontological reasoning. For example, Bryman ( 2008 ) argued that qualitative researchers also call on quantitative thinking, but tend to use somewhat vague, imprecise terms rather than numbers and percentages—referring to frequencies via the use of phrases such as “more than” and “less than.” Kracauer ( 1952 ) advanced various arguments against the view that CTA was strictly a quantitative method, suggesting that very often we wished to assess content as being negative or positive with respect to some political, social, or economic thesis and that such evaluations could never be merely statistical. He further argued that we often wished to study “underlying” messages or latent content of documentation and that, in consequence, we needed to interpret content as well as count items of content. Morgan ( 1993 ) argued that, given the emphasis that is placed on “coding” in almost all forms of qualitative data analysis, the deployment of counting techniques is essential and we ought therefore to think in terms of what he calls qualitative as well as quantitative content analysis. Naturally, some of these positions create more problems than they seemingly solve (as is the case with considerations of “latent content”), but given the 21st-century predilection for mixed methods research (Creswell, 2007 ), it is clear that CTA has a role to play in integrating quantitative and qualitative modes of analysis in a systematic rather than merely ad hoc and piecemeal fashion. In the sections that follow, I will provide some examples of the ways in which “qualitative” analysis can be combined with systematic modes of counting. First, however, we must focus on what is analyzed in CTA.

Units of Analysis

So, what is the unit of analysis in CTA? A brief answer is that analysis can be focused on words, sentences, grammatical structures, tenses, clauses, ratios (of, say, nouns to verbs), or even “themes.” Berelson ( 1952 ) gave examples of all of the above and also recommended a form of thematic analysis (cf., Braun & Clarke, 2006 ) as a viable option. Other possibilities include counting column length (of speeches and newspaper articles), amounts of (advertising) space, or frequency of images. For our purposes, however, it might be useful to consider a specific (and somewhat traditional) example. Here it is. It is an extract from what has turned out to be one of the most important political speeches of the current century.

Iraq continues to flaunt its hostility toward America and to support terror. The Iraqi regime has plotted to develop anthrax and nerve gas and nuclear weapons for over a decade. This is a regime that has already used poison gas to murder thousands of its own citizens, leaving the bodies of mothers huddled over their dead children. This is a regime that agreed to international inspections then kicked out the inspectors. This is a regime that has something to hide from the civilized world. States like these, and their terrorist allies, constitute an axis of evil, arming to threaten the peace of the world. By seeking weapons of mass destruction, these regimes pose a grave and growing danger. They could provide these arms to terrorists, giving them the means to match their hatred. They could attack our allies or attempt to blackmail the United States. In any of these cases, the price of indifference would be catastrophic. (George W. Bush, State of the Union address, January 29, 2002)

A number of possibilities arise for analyzing the content of a speech such as the one above. Clearly, words and sentences must play a part in any such analysis, but in addition to words, there are structural features of the speech that could also figure. For example, the extract takes the form of a simple narrative—pointing to a past, a present, and an ominous future (catastrophe)—and could therefore be analyzed as such. There are, in addition, several interesting oppositions in the speech (such as those between “regimes” and the “civilized” world), as well as a set of interconnected present participles such as “plotting,” “hiding,” “arming,” and “threatening” that are associated both with Iraq and with other states that “constitute an axis of evil.” Evidently, simple word counts would fail to capture the intricacies of a speech of this kind. Indeed, our example serves another purpose—to highlight the difficulty that often arises in dissociating CTA from discourse analysis (of which narrative analysis and the analysis of rhetoric and trope are subspecies). So how might we deal with these problems?

One approach that can be adopted is to focus on what is referenced in text and speech, that is, to concentrate on the characters or elements that are recruited into the text and to examine the ways in which they are connected or co-associated. I shall provide some examples of this form of analysis shortly. Let us merely note for the time being that in the previous example we have a speech in which various “characters”—including weapons in general, specific weapons (such as nerve gas), threats, plots, hatred, evil, and mass destruction—play a role. Be aware that we need not be concerned with the veracity of what is being said—whether it is true or false—but simply with what is in the speech and how what is in there is associated. (We may leave the task of assessing truth and falsity to the jurists). Be equally aware that it is a text that is before us and not an insight into the ex-president’s mind, or his thinking, or his beliefs, or any other subjective property that he may have possessed.

In the introductory paragraph, I made brief reference to some ideas of the German philosopher Jürgen Habermas ( 1987 ). It is not my intention here to expand on the detailed twists and turns of his claims with respect to the role of language in the “lifeworld” at this point. However, I do intend to borrow what I regard as some particularly useful ideas from his work. The first is his claim—influenced by a strong line of 20th-century philosophical thinking—that language and culture are constitutive of the lifeworld (Habermas, 1987 , p. 125), and in that sense we might say that things (including individuals and societies) are made in language. That is a simple justification for focusing on what people say rather than what they “think” or “believe” or “feel” or “mean” (all of which have been suggested at one time or another as points of focus for social inquiry and especially qualitative forms of inquiry). Second, Habermas argued that speakers and therefore hearers (and, one might add, writers and therefore readers), in what he calls their speech acts, necessarily adopt a pragmatic relation to one of three worlds: entities in the objective world, things in the social world, and elements of a subjective world. In practice, Habermas ( 1987 , p. 120) suggested all three worlds are implicated in any speech act, but that there will be a predominant orientation to one of them. To rephrase this in a crude form, when speakers engage in communication, they refer to things and facts and observations relating to external nature, to aspects of interpersonal relations, and to aspects of private inner subjective worlds (thoughts, feelings, beliefs, etc.). One of the problems with locating CTA in “communication research” has been that the communications referred to are but a special and limited form of action (often what Habermas called strategic acts). In other words, television, newspaper, video, and Internet communications are just particular forms (with particular features) of action in general. Again, we might note in passing that the adoption of the Habermassian perspective on speech acts implies that much of qualitative analysis in particular has tended to focus only on one dimension of communicative action—the subjective and private. In this respect, I would argue that it is much better to look at speeches such as George W Bush’s 2002 State of the Union address as an “account” and to examine what has been recruited into the account, and how what has been recruited is connected or co-associated, rather than use the data to form insights into his (or his adviser’s) thoughts, feelings, and beliefs.

In the sections that follow, and with an emphasis on the ideas that I have just expounded, I intend to demonstrate how CTA can be deployed to advantage in almost all forms of inquiry that call on either interview (or speech-based) data or textual data. In my first example, I will show how CTA can be used to analyze a group of interviews. In the second example, I will show how it can be used to analyze a group of policy documents. In the third, I shall focus on a single interview (a “case”), and in the fourth and final example, I will show how CTA can be used to track the biography of a concept. In each instance, I shall briefly introduce the context of the “problem” on which the research was based, outline the methods of data collection, discuss how the data were analyzed and presented, and underline the ways in which CTA has sharpened the analytical strategy.

Analyzing a Sample of Interviews: Looking at Concepts and Their Co-associations in a Semantic Network

My first example of using CTA is based on a research study that was initially undertaken in the early 2000s. It was a project aimed at understanding why older people might reject the offer to be immunized against influenza (at no cost to them). The ultimate objective was to improve rates of immunization in the study area. The first phase of the research was based on interviews with 54 older people in South Wales. The sample included people who had never been immunized, some who had refused immunization, and some who had accepted immunization. Within each category, respondents were randomly selected from primary care physician patient lists, and the data were initially analyzed “thematically” and published accordingly (Evans, Prout, Prior, Tapper-Jones, & Butler, 2007 ). A few years later, however, I returned to the same data set to look at a different question—how (older) lay people talked about colds and flu, especially how they distinguished between the two illnesses and how they understood the causes of the two illnesses (see Prior, Evans, & Prout, 2011 ). Fortunately, in the original interview schedule, we had asked people about how they saw the “differences between cold and flu” and what caused flu, so it was possible to reanalyze the data with such questions in mind. In that frame, the example that follows demonstrates not only how CTA might be used on interview data, but also how it might be used to undertake a secondary analysis of a preexisting data set (Bryman, 2008 ).

As with all talk about illness, talk about colds and flu is routinely set within a mesh of concerns—about causes, symptoms, and consequences. Such talk comprises the base elements of what has at times been referred to as the “explanatory model” of an illness (Kleinman, Eisenberg, & Good, 1978 ). In what follows, I shall focus almost entirely on issues of causation as understood from the viewpoint of older people; the analysis is based on the answers that respondents made in response to the question, “How do you think people catch flu?”

Semistructured interviews of the kind undertaken for a study such as this are widely used and are often characterized as akin to “a conversation with a purpose” (Kahn & Cannell, 1957 , p. 97). One of the problems of analyzing the consequent data is that, although the interviewer holds to a planned schedule, the respondents often reflect in a somewhat unstructured way about the topic of investigation, so it is not always easy to unravel the web of talk about, say, “causes” that occurs in the interview data. In this example, causal agents of flu, inhibiting agents, and means of transmission were often conflated by the respondents. Nevertheless, in their talk people did answer the questions that were posed, and in the study referred to here, that talk made reference to things such as “bugs” (and “germs”) as well as viruses, but the most commonly referred to causes were “the air” and the “atmosphere.” The interview data also pointed toward means of transmission as “cause”—so coughs and sneezes and mixing in crowds figured in the causal mix. Most interesting, perhaps, was the fact that lay people made a nascent distinction between facilitating factors (such as bugs and viruses) and inhibiting factors (such as being resistant, immune, or healthy), so that in the presence of the latter, the former are seen to have very little effect. Here are some shorter examples of typical question–response pairs from the original interview data.

(R:32): “How do you catch it [the flu]? Well, I take it its through ingesting and inhaling bugs from the atmosphere. Not from sort of contact or touching things. Sort of airborne bugs. Is that right?” (R:3): “I suppose it’s [the cause of flu] in the air. I think I get more diseases going to the surgery than if I stayed home. Sometimes the waiting room is packed and you’ve got little kids coughing and spluttering and people sneezing, and air conditioning I think is a killer by and large I think air conditioning in lots of these offices.” (R:46): “I think you catch flu from other people. You know in enclosed environments in air conditioning which in my opinion is the biggest cause of transferring diseases is air conditioning. Worse thing that was ever invented that was. I think so, you know. It happens on aircraft exactly the same you know.”

Alternatively, it was clear that for some people being cold, wet, or damp could also serve as a direct cause of flu; thus: Interviewer: “OK, good. How do you think you catch the flu?”

(R:39): “Ah. The 65 dollar question. Well, I would catch it if I was out in the rain and I got soaked through. Then I would get the flu. I mean my neighbour up here was soaked through and he got pneumonia and he died. He was younger than me: well, 70. And he stayed in his wet clothes and that’s fatal. Got pneumonia and died, but like I said, if I get wet, especially if I get my head wet, then I can get a nasty head cold and it could develop into flu later.”

As I suggested earlier, despite the presence of bugs and germs, viruses, the air, and wetness or dampness, “catching” the flu is not a matter of simple exposure to causative agents. Thus, some people hypothesized that within each person there is a measure of immunity or resistance or healthiness that comes into play and that is capable of counteracting the effects of external agents. For example, being “hardened” to germs and harsh weather can prevent a person getting colds and flu. Being “healthy” can itself negate the effects of any causative agents, and healthiness is often linked to aspects of “good” nutrition and diet and not smoking cigarettes. These mitigating and inhibiting factors can either mollify the effects of infection or prevent a person “catching” the flu entirely. Thus, (R:45) argued that it was almost impossible for him to catch flu or cold “cos I got all this resistance.” Interestingly, respondents often used possessive pronouns in their discussion of immunity and resistance (“my immunity” and “my resistance”)—and tended to view them as personal assets (or capital) that might be compromised by mixing with crowds.

By implication, having a weak immune system can heighten the risk of contracting colds and flu and might therefore spur one to take preventive measures, such as accepting a flu shot. Some people believe that the flu shot can cause the flu and other illnesses. An example of what might be called lay “epidemiology” (Davison, Davey-Smith, & Frankel, 1991 ) is evident in the following extract.

(R:4): “Well, now it’s coincidental you know that [my brother] died after the jab, but another friend of mine, about 8 years ago, the same happened to her. She had the jab and about six months later, she died, so I know they’re both coincidental, but to me there’s a pattern.”

Normally, results from studies such as this are presented in exactly the same way as has just been set out. Thus, the researcher highlights given themes that are said to have emerged from the data and then provides appropriate extracts from the interviews to illustrate and substantiate the relevant themes. However, one reasonable question that any critic might ask about the selected data extracts concerns the extent to which they are “representative” of the material in the data set as a whole. Maybe, for example, the author has been unduly selective in his or her use of both themes and quotations. Perhaps, as a consequence, the author has ignored or left out talk that does not fit the arguments or extracts that might be considered dull and uninteresting compared to more exotic material. And these kinds of issues and problems are certainly common to the reporting of almost all forms of qualitative research. However, the adoption of CTA techniques can help to mollify such problems. This is so because, by using CTA, we can indicate the extent to which we have used all or just some of the data, and we can provide a view of the content of the entire sample of interviews rather than just the content and flavor of merely one or two interviews. In this light, we must consider Figure 19.1 , which is based on counting the number of references in the 54 interviews to the various “causes” of the flu, though references to the flu shot (i.e., inoculation) as a cause of flu have been ignored for the purpose of this discussion. The node sizes reflect the relative importance of each cause as determined by the concept count (frequency of occurrence). The links between nodes reflect the degree to which causes are co-associated in interview talk and are calculated according to a co-occurrence index (see, e.g., SPSS, 2007 , p. 183).

What causes flu? A lay perspective. Factors listed as causes of colds and flu in 54 interviews. Node size is proportional to number of references “as causes.” Line thickness is proportional to co-occurrence of any two “causes” in the set of interviews.

Given this representation, we can immediately assess the relative importance of the different causes as referred to in the interview data. Thus, we can see that such things as (poor) “hygiene” and “foreigners” were mentioned as a potential cause of flu—but mention of hygiene and foreigners was nowhere near as important as references to “the air” or to “crowds” or to “coughs and sneezes.” In addition, we can also determine the strength of the connections that interviewees made between one cause and another. Thus, there are relatively strong links between “resistance” and “coughs and sneezes,” for example.

In fact, Figure 19.1 divides causes into the “external” and the “internal,” or the facilitating and the impeding (lighter and darker nodes). Among the former I have placed such things as crowds, coughs, sneezes, and the air, while among the latter I have included “resistance,” “immunity,” and “health.” That division is a product of my conceptualizing and interpreting the data, but whichever way we organize the findings, it is evident that talk about the causes of flu belongs in a web or mesh of concerns that would be difficult to represent using individual interview extracts alone. Indeed, it would be impossible to demonstrate how the semantics of causation belong to a culture (rather than to individuals) in any other way. In addition, I would argue that the counting involved in the construction of the diagram functions as a kind of check on researcher interpretations and provides a source of visual support for claims that an author might make about, say, the relative importance of “damp” and “air” as perceived causes of disease. Finally, the use of CTA techniques allied with aspects of conceptualization and interpretation has enabled us to approach the interview data as a set and to consider the respondents as belonging to a community, rather than regarding them merely as isolated and disconnected individuals, each with their own views. It has also enabled us to squeeze some new findings out of old data, and I would argue that it has done so with advantage. There are other advantages to using CTA to explore data sets, which I will highlight in the next section.

Analyzing a Sample of Documents: Using Content Analysis to Verify Claims

Policy analysis is a difficult business. To begin, it is never entirely clear where (social, health, economic, environmental) policy actually is. Is it in documents (as published by governments, think tanks, and research centers), in action (what people actually do), or in speech (what people say)? Perhaps it rests in a mixture of all three realms. Yet, wherever it may be, it is always possible, at the very least, to identify a range of policy texts and to focus on the conceptual or semantic webs in terms of which government officials and other agents (such as politicians) talk about the relevant policy issues. Furthermore, insofar as policy is recorded—in speeches, pamphlets, and reports—we may begin to speak of specific policies as having a history or a pedigree that unfolds through time (think, e.g., of U.S. or U.K. health policies during the Clinton years or the Obama years). And, insofar as we consider “policy” as having a biography or a history, we can also think of studying policy narratives.

Though firmly based in the world of literary theory, narrative method has been widely used for both the collection and the analysis of data concerning ways in which individuals come to perceive and understand various states of health, ill health, and disability (Frank, 1995 ; Hydén, 1997 ). Narrative techniques have also been adapted for use in clinical contexts and allied to concepts of healing (Charon, 2006 ). In both social scientific and clinical work, however, the focus is invariably on individuals and on how individuals “tell” stories of health and illness. Yet narratives can also belong to collectives—such as political parties and ethnic and religious groups—just as much as to individuals, and in the latter case there is a need to collect and analyze data that are dispersed across a much wider range of materials than can be obtained from the personal interview. In this context, Roe ( 1994 ) demonstrated how narrative method can be applied to an analysis of national budgets, animal rights, and environmental policies.

An extension of the concept of narrative to policy discourse is undoubtedly useful (Newman & Vidler, 2006 ), but how might such narratives be analyzed? What strategies can be used to unravel the form and content of a narrative, especially in circumstances where the narrative might be contained in multiple (policy) documents, authored by numerous individuals, and published across a span of time rather than in a single, unified text such as a novel? Roe ( 1994 ), unfortunately, was not in any way specific about analytical procedures, apart from offering the useful rule to “never stray too far from the data” (p. xii). So, in this example, I will outline a strategy for tackling such complexities. In essence, it is a strategy that combines techniques of linguistically (rule) based CTA with a theoretical and conceptual frame that enables us to unravel and identify the core features of a policy narrative. My substantive focus is on documents concerning health service delivery policies published from 2000 to 2009 in the constituent countries of the United Kingdom (that is, England, Scotland, Wales, and Northern Ireland—all of which have different political administrations).

Narratives can be described and analyzed in various ways, but for our purposes we can say that they have three key features: they point to a chronology, they have a plot, and they contain “characters.”

All narratives have beginnings; they also have middles and endings, and these three stages are often seen as comprising the fundamental structure of narrative text. Indeed, in his masterly analysis of time and narrative, Ricoeur ( 1984 ) argued that it is in the unfolding chronological structure of a narrative that one finds its explanatory (and not merely descriptive) force. By implication, one of the simplest strategies for the examination of policy narratives is to locate and then divide a narrative into its three constituent parts—beginning, middle, and end.

Unfortunately, while it can sometimes be relatively easy to locate or choose a beginning to a narrative, it can be much more difficult to locate an end point. Thus, in any illness narrative, a narrator might be quite capable of locating the start of an illness process (in an infection, accident, or other event) but unable to see how events will be resolved in an ongoing and constantly unfolding life. As a consequence, both narrators and researchers usually find themselves in the midst of an emergent present—a present without a known and determinate end (see, e.g., Frank, 1995 ). Similar considerations arise in the study of policy narratives where chronology is perhaps best approached in terms of (past) beginnings, (present) middles, and projected futures.

According to Ricoeur ( 1984 ), our basic ideas about narrative are best derived from the work and thought of Aristotle, who in his Poetics sought to establish “first principles” of composition. For Ricoeur, as for Aristotle, plot ties things together. It “brings together factors as heterogeneous as agents, goals, means, interactions, circumstances, unexpected results” (p. 65) into the narrative frame. For Aristotle, it is the ultimate untying or unraveling of the plot that releases the dramatic energy of the narrative.

Characters are most commonly thought of as individuals, but they can be considered in much broader terms. Thus, the French semiotician A. J. Greimas ( 1970 ), for example, suggested that, rather than think of characters as people, it would be better to think in terms of what he called actants and of the functions that such actants fulfill within a story. In this sense, geography, climate, and capitalism can be considered characters every bit as much as aggressive wolves and Little Red Riding Hood. Further, he argued that the same character (actant) can be considered to fulfill many functions, and the same function may be performed by many characters. Whatever else, the deployment of the term actant certainly helps us to think in terms of narratives as functioning and creative structures. It also serves to widen our understanding of the ways in which concepts, ideas, and institutions, as well “things” in the material world, can influence the direction of unfolding events every bit as much as conscious human subjects. Thus, for example, the “American people,” “the nation,” “the Constitution,” “the West,” “tradition,” and “Washington” can all serve as characters in a policy story.

As I have already suggested, narratives can unfold across many media and in numerous arenas—speech and action, as well as text. Here, however, my focus is solely on official documents—all of which are U.K. government policy statements, as listed in Table 19.1 . The question is, How might CTA help us unravel the narrative frame?

It might be argued that a simple reading of any document should familiarize the researcher with elements of all three policy narrative components (plot, chronology, and character). However, in most policy research, we are rarely concerned with a single and unified text, as is the case with a novel; rather, we have multiple documents written at distinctly different times by multiple (usually anonymous) authors that notionally can range over a wide variety of issues and themes. In the full study, some 19 separate publications were analyzed across England, Wales, Scotland, and Northern Ireland.

Naturally, listing word frequencies—still less identifying co-occurrences and semantic webs in large data sets (covering hundreds of thousands of words and footnotes)—cannot be done manually, but rather requires the deployment of complex algorithms and text-mining procedures. To this end, I analyzed the 19 documents using “Text Mining for Clementine” (SPSS, 2007 ).

Text-mining procedures begin by providing an initial list of concepts based on the lexicon of the text but that can be weighted according to word frequency and that take account of elementary word associations. For example, learning disability, mental health, and performance management indicate three concepts, not six words. Using such procedures on the aforementioned documents gives the researcher an initial grip on the most important concepts in the document set of each country. Note that this is much more than a straightforward concordance analysis of the text and is more akin to what Ryan and Bernard ( 2000 ) referred to as semantic analysis and Carley ( 1993 ) has referred to as concept and mapping analysis.

So, the first task was to identify and then extract the core concepts, thus identifying what might be called “key” characters or actants in each of the policy narratives. For example, in the Scottish documents, such actants included “Scotland” and the “Scottish people,” as well as “health” and the “National Health Service (NHS),” among others, while in the Welsh documents it was “the people of Wales” and “Wales” that figured largely—thus emphasizing how national identity can play every bit as important a role in a health policy narrative as concepts such as “health,” “hospitals,” and “well-being.”

Having identified key concepts, it was then possible to track concept clusters in which particular actants or characters are embedded. Such cluster analysis is dependent on the use of co-occurrence rules and the analysis of synonyms, whereby it is possible to get a grip on the strength of the relationships between the concepts, as well as the frequency with which the concepts appear in the collected texts. In Figure 19.2 , I provide an example of a concept cluster. The diagram indicates the nature of the conceptual and semantic web in which various actants are discussed. The diagrams further indicate strong (solid line) and weaker (dashed line) connections between the various elements in any specific mix, and the numbers indicate frequency counts for the individual concepts. Using Clementine , the researcher is unable to specify in advance which clusters will emerge from the data. One cannot, for example, choose to have an NHS cluster. In that respect, these diagrams not only provide an array in terms of which concepts are located, but also serve as a check on and to some extent validation of the interpretations of the researcher. None of this tells us what the various narratives contained within the documents might be, however. They merely point to key characters and relationships both within and between the different narratives. So, having indicated the techniques used to identify the essential parts of the four policy narratives, it is now time to sketch out their substantive form.

Concept cluster for “care” in six English policy documents, 2000–2007. Line thickness is proportional to the strength co-occurrence coefficient. Node size reflects relative frequency of concept, and (numbers) refer to the frequency of concept. Solid lines indicate relationships between terms within the same cluster, and dashed lines indicate relationships between terms in different clusters.

It may be useful to note that Aristotle recommended brevity in matters of narrative—deftly summarizing the whole of the Odyssey in just seven lines. In what follows, I attempt—albeit somewhat weakly—to emulate that example by summarizing a key narrative of English health services policy in just four paragraphs. Note how the narrative unfolds in relation to the dates of publication. In the English case (though not so much in the other U.K. countries), it is a narrative that is concerned to introduce market forces into what is and has been a state-managed health service. Market forces are justified in terms of improving opportunities for the consumer (i.e., the patients in the service), and the pivot of the newly envisaged system is something called “patient choice” or “choice.” This is how the story unfolds as told through the policy documents between 2000 and 2008 (see Table 19.1 ). The citations in the following paragraphs are to the Department of Health publications (by year) listed in Table 19.1 .

The advent of the NHS in 1948 was a “seminal event” (2000, p. 8), but under successive Conservative administrations, the NHS was seriously underfunded (2006, p. 3). The (New Labour) government will invest (2000) or already has (2003, p. 4) invested extensively in infrastructure and staff, and the NHS is now on a “journey of major improvement” (2004, p. 2). But “more money is only a starting point” (2000, p. 2), and the journey is far from finished. Continuation requires some fundamental changes of “culture” (2003, p. 6). In particular, the NHS remains unresponsive to patient need, and “all too often, the individual needs and wishes are secondary to the convenience of the services that are available. This ‘one size fits all’ approach is neither responsive, equitable nor person-centred” (2003, p. 17). In short, the NHS is a 1940s system operating in a 21st-century world (2000, p. 26). Change is therefore needed across the “whole system” (2005, p. 3) of care and treatment.

Above all, we must recognize that we “live in a consumer age” (2000, p. 26). People’s expectations have changed dramatically (2006, p. 129), and people want more choice, more independence, and more control (2003, p. 12) over their affairs. Patients are no longer, and should not be considered, “passive recipients” of care (2003, p. 62), but wish to be and should be (2006, p. 81) actively “involved” in their treatments (2003, p. 38; 2005, p. 18)—indeed, engaged in a partnership (2003, p. 22) of respect with their clinicians. Furthermore, most people want a personalized service “tailor made to their individual needs” (2000, p. 17; 2003, p. 15; 2004, p. 1; 2006, p. 83)—“a service which feels personal to each and every individual within a framework of equity and good use of public money” (2003, p. 6).

To advance the necessary changes, “patient choice” must be and “will be strengthened” (2000, p. 89). “Choice” must be made to “happen” (2003), and it must be “real” (2003, p. 3; 2004, p. 5; 2005, p. 20; 2006, p. 4). Indeed, it must be “underpinned” (2003, p. 7) and “widened and deepened” (2003, p. 6) throughout the entire system of care.

If “we” expand and underpin patient choice in appropriate ways and engage patients in their treatment systems, then levels of patient satisfaction will increase (2003, p. 39), and their choices will lead to a more “efficient” (2003, p. 5; 2004, p. 2; 2006, p. 16) and effective (2003, p. 62; 2005, p. 8) use of resources. Above all, the promotion of choice will help to drive up “standards” of care and treatment (2000, p. 4; 2003, p. 12; 2004, p. 3; 2005, p. 7; 2006, p. 3). Furthermore, the expansion of choice will serve to negate the effects of the “inverse care law,” whereby those who need services most tend to get catered to the least (2000, p. 107; 2003, p. 5; 2006, p. 63), and it will thereby help in moderating the extent of health inequalities in the society in which we live. “The overall aim of all our reforms,” therefore, “is to turn the NHS from a top down monolith into a responsive service that gives the patient the best possible experience. We need to develop an NHS that is both fair to all of us, and personal to each of us” (2003, p. 5).

We can see how most—though not all—of the elements of this story are represented in Figure 19.2. In particular, we can see strong (co-occurrence) links between care and choice and how partnership, performance, control, and improvement have a prominent profile. There are some elements of the web that have a strong profile (in terms of node size and links), but to which we have not referred; access, information, primary care, and waiting times are four. As anyone well versed in English healthcare policy would know, these elements have important roles to play in the wider, consumer-driven narrative. However, by rendering the excluded as well as included elements of that wider narrative visible, the concept web provides a degree of verification on the content of the policy story as told herein and on the scope of its “coverage.”

In following through on this example, we have moved from CTA to a form of discourse analysis (in this instance, narrative analysis). That shift underlines aspects of both the versatility of CTA and some of its weaknesses—versatility in the sense that CTA can be readily combined with other methods of analysis and in the way in which the results of the CTA help us to check and verify the claims of the researcher. The weakness of the diagram compared to the narrative is that CTA on its own is a somewhat one-dimensional and static form of analysis, and while it is possible to introduce time and chronology into the diagrams, the diagrams themselves remain lifeless in the absence of some form of discursive overview. (For a fuller analysis of these data, see Prior, Hughes, & Peckham, 2012 ).

Analyzing a Single Interview: The Role of Content Analysis in a Case Study

So far, I have focused on using CTA on a sample of interviews and a sample of documents. In the first instance, I recommended CTA for its capacity to tell us something about what is seemingly central to interviewees and for demonstrating how what is said is linked (in terms of a concept network). In the second instance, I reaffirmed the virtues of co-occurrence and network relations, but this time in the context of a form of discourse analysis. I also suggested that CTA can serve an important role in the process of verification of a narrative and its academic interpretation. In this section, however, I am going to link the use of CTA to another style of research—case study—to show how CTA might be used to analyze a single “case.”

Case study is a term used in multiple and often ambiguous ways. However, Gerring ( 2004 ) defined it as “an intensive study of a single unit for the purpose of understanding a larger class of (similar) units” (p. 342). As Gerring pointed out, case study does not necessarily imply a focus on N = 1, although that is indeed the most logical number for case study research (Ragin & Becker, 1992 ). Naturally, an N of 1 can be immensely informative, and whether we like it or not, we often have only one N to study (think, e.g., of the 1986 Challenger shuttle disaster or of the 9/11 attack on the World Trade Center). In the clinical sciences, case studies are widely used to represent the “typical” features of a wider class of phenomena and often used to define a kind or syndrome (as in the field of clinical genetics). Indeed, at the risk of mouthing a tautology, one can say that the distinctive feature of case study is its focus on a case in all of its complexity—rather than on individual variables and their interrelationships, which tends to be a point of focus for large N research.

There was a time when case study was central to the science of psychology. Breuer and Freud’s (2001) famous studies of “hysteria” (originally published in 1895) provide an early and outstanding example of the genre in this respect, but as with many of the other styles of social science research, the influence of case studies waned with the rise of much more powerful investigative techniques—including experimental methods—driven by the deployment of new statistical technologies. Ideographic studies consequently gave way to the current fashion for statistically driven forms of analysis that focus on causes and cross-sectional associations between variables rather than ideographic complexity.

In the example that follows, we will look at the consequences of a traumatic brain injury (TBI) on just one individual. The analysis is based on an interview with a person suffering from such an injury, and it was one of 32 interviews carried out with people who had experienced a TBI. The objective of the original research was to develop an outcome measure for TBI that was sensitive to the sufferer’s (rather than the health professional’s) point of view. In our original study (see Morris et al., 2005 ), interviews were also undertaken with 27 carers of the injured with the intention of comparing their perceptions of TBI to those of the people for whom they cared. A sample survey was also undertaken to elicit views about TBI from a much wider population of patients than was studied via interview.

In the introduction, I referred to Habermas and the concept of the lifeworld. Lifeworld ( Lebenswelt ) is a concept that first arose from 20th-century German philosophy. It constituted a specific focus for the work of Alfred Schutz (see, e.g., Schutz & Luckman, 1974 ). Schutz ( 1974 ) described the lifeworld as “that province of reality which the wide-awake and normal adult simply takes-for-granted in an attitude of common sense” (p. 3). Indeed, it was the routine and taken-for-granted quality of such a world that fascinated Schutz. As applied to the worlds of those with head injuries, the concept has particular resonance because head injuries often result in that taken-for-granted quality being disrupted and fragmented, ending in what Russian neuropsychologist A. R. Luria ( 1975 ) once described as “shattered” worlds. As well as providing another excellent example of a case study, Luria’s work is also pertinent because he sometimes argued for a “romantic science” of brain injury—that is, a science that sought to grasp the worldview of the injured patient by paying attention to an unfolding and detailed personal “story” of the individual with the head injury as well as to the neurological changes and deficits associated with the injury itself. In what follows, I shall attempt to demonstrate how CTA might be used to underpin such an approach.

In the original research, we began analysis by a straightforward reading of the interview transcripts. Unfortunately, a simple reading of a text or an interview can, strangely, mislead the reader into thinking that some issues or themes are more important than is warranted by the contents of the text. How that comes about is not always clear, but it probably has something to do with a desire to develop “findings” and our natural capacity to overlook the familiar in favor of the unusual. For that reason alone, it is always useful to subject any text to some kind of concordance analysis—that is, generating a simple frequency list of words used in an interview or text. Given the current state of technology, one might even speak these days of using text-mining procedures such as the aforementioned Clementine to undertake such a task. By using Clementine , and as we have seen, it is also possible to measure the strength of co-occurrence links between elements (i.e., words and concepts) in the entire data set (in this example, 32 interviews), though for a single interview these aims can just as easily be achieved using much simpler, low-tech strategies.

By putting all 32 interviews into the database, several common themes emerged. For example, it was clear that “time” entered into the semantic web in a prominent manner, and it was clearly linked to such things as “change,” “injury,” “the body,” and what can only be called the “I was.” Indeed, time runs through the 32 stories in many guises, and the centrality of time is a reflection of storytelling and narrative recounting in general—chronology, as we have noted, being a defining feature of all storytelling (Ricoeur, 1984 ). Thus, sufferers both recounted the events surrounding their injury and provided accounts as to how the injuries affected their current life and future hopes. As to time present, much of the patient story circled around activities of daily living—walking, working, talking, looking, feeling, remembering, and so forth.

Understandably, the word and the concept of “injury” featured largely in the interviews, though it was a word most commonly associated with discussions of physical consequences of injury. There were many references in that respect to injured arms, legs, hands, and eyes. There were also references to “mind”—though with far less frequency than with references to the body and to body parts. Perhaps none of this is surprising. However, one of the most frequent concepts in the semantic mix was the “I was” (716 references). The statement “I was,” or “I used to” was, in turn, strongly connected to terms such as “the accident” and “change.” Interestingly, the “I was” overwhelmingly eclipsed the “I am” in the interview data (the latter with just 63 references). This focus on the “I was” appears in many guises. For example, it is often associated with the use of the passive voice: “I was struck by a car,” “I was put on the toilet,” “I was shipped from there then, transferred to [Cityville],” “I got told that I would never be able …,” “I was sat in a room,” and so forth. In short, the “I was” is often associated with things, people, and events acting on the injured person. More important, however, the appearance of the “I was” is often used to preface statements signifying a state of loss or change in the person’s course of life—that is, as an indicator for talk about the patient’s shattered world. For example, Patient 7122 stated,

The main (effect) at the moment is I’m not actually with my children, I can’t really be their mum at the moment. I was a caring Mum, but I can’t sort of do the things that I want to be able to do like take them to school. I can’t really do a lot on my own. Like crossing the roads.

Another patient stated,

Everything is completely changed. The way I was … I can’t really do anything at the moment. I mean my German, my English, everything’s gone. Job possibilities is out the window. Everything is just out of the window … I just think about it all the time actually every day you know. You know it has destroyed me anyway, but if I really think about what has happened I would just destroy myself.

Each of these quotations, in its own way, serves to emphasize how life has changed and how the patient’s world has changed. In that respect, we can say that one of the major outcomes arising from TBI may be substantial “biographical disruption” (Bury, 1982 ), whereupon key features of an individual’s life course are radically altered forever. Indeed, as Becker ( 1997 , p. 37) argued in relation to a wide array of life events, “When their health is suddenly disrupted, people are thrown into chaos. Illness challenges one’s knowledge of one’s body. It defies orderliness. People experience the time before their illness and its aftermath as two separate entities.” Indeed, this notion of a cusp in personal biography is particularly well illustrated by Luria’s patient Zasetsky; the latter often refers to being a “newborn creature” (Luria, 1975 , pp. 24, 88), a shadow of a former self (p. 25), and as having his past “wiped out” (p. 116).

However, none of this tells us about how these factors come together in the life and experience of one individual. When we focus on an entire set of interviews, we necessarily lose the rich detail of personal experience and tend instead to rely on a conceptual rather than a graphic description of effects and consequences (to focus on, say, “memory loss,” rather than loss of memory about family life). The contents of Figure 19.3 attempt to correct that vision. Figure 19.3 records all the things that a particular respondent (Patient 7011) used to do and liked doing. It records all the things that he says he can no longer do (at 1 year after injury), and it records all the consequences that he suffered from his head injury at the time of the interview. Thus, we see references to epilepsy (his “fits”), paranoia (the patient spoke of his suspicions concerning other people, people scheming behind his back, and his inability to trust others), deafness, depression, and so forth. Note that, although I have inserted a future tense into the web (“I will”), such a statement never appeared in the transcript. I have set it there for emphasis and to show how, for this person, the future fails to connect to any of the other features of his world except in a negative way. Thus, he states at one point that he cannot think of the future because it makes him feel depressed (see Figure 19.3 ). The line thickness of the arcs reflects the emphasis that the subject placed on the relevant “outcomes” in relation to the “I was” and the “now” during the interview. Thus, we see that factors affecting his concentration and balance loom large, but that he is also concerned about his being dependent on others, his epileptic fits, and his being unable to work and drive a vehicle. The schism in his life between what he used to do, what he cannot now do, and his current state of being is nicely represented in the CTA diagram.

The shattered world of Patient 7011. Thickness of lines (arcs) is proportional to the frequency of reference to the “outcome” by the patient during the interview.

What have we gained from executing this kind of analysis? For a start, we have moved away from a focus on variables, frequencies, and causal connections (e.g., a focus on the proportion of people with TBI who suffer from memory problems or memory problems and speech problems) and refocused on how the multiple consequences of a TBI link together in one person. In short, instead of developing a narrative of acting variables, we have emphasized a narrative of an acting individual (Abbott, 1992 , p. 62). Second, it has enabled us to see how the consequences of a TBI connect to an actual lifeworld (and not simply an injured body). So the patient is not viewed just as having a series of discrete problems such as balancing, or staying awake, which is the usual way of assessing outcomes, but as someone struggling to come to terms with an objective world of changed things, people, and activities (missing work is not, for example, routinely considered an outcome of head injury). Third, by focusing on what the patient was saying, we gain insight into something that is simply not visible by concentrating on single outcomes or symptoms alone—namely, the void that rests at the center of the interview, what I have called the “I was.” Fourth, we have contributed to understanding a type, because the case that we have read about is not simply a case of “John” or “Jane” but a case of TBI, and in that respect it can add to many other accounts of what it is like to experience head injury—including one of the most well documented of all TBI cases, that of Zatetsky. Finally, we have opened up the possibility of developing and comparing cognitive maps (Carley, 1993 ) for different individuals and thereby gained insight into how alternative cognitive frames of the world arise and operate.

Tracing the Biography of a Concept

In the previous sections, I emphasized the virtues of CTA for its capacity to link into a data set in its entirety—and how the use of CTA can counter any tendency of a researcher to be selective and partial in the presentation and interpretation of information contained in interviews and documents. However, that does not mean that we always must take an entire document or interview as the data source. Indeed, it is possible to select (on rational and explicit grounds) sections of documentation and to conduct the CTA on the chosen portions. In the example that follows, I do just that. The sections that I chose to concentrate on are titles and abstracts of academic papers—rather than the full texts. The research on which the following is based is concerned with a biography of a concept and is being conducted in conjunction with a Ph.D. student of mine, Joanne Wilson. Joanne thinks of this component of the study more in terms of a “scoping study” than of a biographical study, and that, too, is a useful framework for structuring the context in which CTA can be used. Scoping studies (Arksey & O’Malley, 2005 ) are increasingly used in health-related research to “map the field” and to get a sense of the range of work that has been conducted on a given topic. Such studies can also be used to refine research questions and research designs. In our investigation, the scoping study was centered on the concept of well-being. Since 2010, well-being has emerged as an important research target for governments and corporations as well as for academics, yet it is far from clear to what the term refers. Given the ambiguity of meaning, it is clear that a scoping review, rather than either a systematic review or a narrative review of available literature, would be best suited to our goals.

The origins of the concept of well-being can be traced at least as far back as the 4th century bc , when philosophers produced normative explanations of the good life (e.g., eudaimonia, hedonia, and harmony). However, contemporary interest in the concept seemed to have been regenerated by the concerns of economists and, most recently, psychologists. These days, governments are equally concerned with measuring well-being to inform policy and conduct surveys of well-being to assess that state of the nation (see, e.g., Office for National Statistics, 2012 )—but what are they assessing?

We adopted a two-step process to address the research question, “What is the meaning of ‘well-being’ in the context of public policy?” First, we explored the existing thesauri of eight databases to establish those higher order headings (if any) under which articles with relevance to well-being might be cataloged. Thus, we searched the following databases: Cumulative Index of Nursing and Allied Health Literature, EconLit, Health Management Information Consortium, Medline, Philosopher’s Index, PsycINFO, Sociological Abstracts, and Worldwide Political Science Abstracts. Each of these databases adopts keyword-controlled vocabularies. In other words, they use inbuilt statistical procedures to link core terms to a set lexis of phrases that depict the concepts contained in the database. Table 19.2 shows each database and its associated taxonomy. The contents of Table 19.2 point toward a linguistic infrastructure in terms of which academic discourse is conducted, and our task was to extract from this infrastructure the semantic web wherein the concept of well-being is situated. We limited the thesaurus terms to well-being and its variants (i.e., wellbeing or well being). If the term was returned, it was then exploded to identify any associated terms.

To develop the conceptual map, we conducted a free-text search for well-being and its variants within the context of public policy across the same databases. We orchestrated these searches across five time frames: January 1990 to December 1994, January 1995 to December 1999, January 2000 to December 2004, January 2005 to December 2009, and January 2010 to October 2011. Naturally, different disciplines use different words to refer to well-being, each of which may wax and wane in usage over time. The searches thus sought to quantitatively capture any changes in the use and subsequent prevalence of well-being and any referenced terms (i.e., to trace a biography).

It is important to note that we did not intend to provide an exhaustive, systematic search of all the relevant literature. Rather, we wanted to establish the prevalence of well-being and any referenced (i.e., allied) terms within the context of public policy. This has the advantage of ensuring that any identified words are grounded in the literature (i.e., they represent words actually used by researchers to talk and write about well-being in policy settings). The searches were limited to abstracts to increase the specificity, albeit at some expense to sensitivity, with which we could identify relevant articles.

We also employed inclusion/exclusion criteria to facilitate the process by which we selected articles, thereby minimizing any potential bias arising from our subjective interpretations. We included independent, stand-alone investigations relevant to the study’s objectives (i.e., concerned with well-being in the context of public policy), which focused on well-being as a central outcome or process and which made explicit reference to “well-being” and “public policy” in either the title or the abstract. We excluded articles that were irrelevant to the study’s objectives, those that used noun adjuncts to focus on the well-being of specific populations (i.e., children, elderly, women) and contexts (e.g., retirement village), and those that focused on deprivation or poverty unless poverty indices were used to understand well-being as opposed to social exclusion. We also excluded book reviews and abstracts describing a compendium of studies.

Using these criteria, Joanne Wilson conducted the review and recorded the results on a template developed specifically for the project, organized chronologically across each database and timeframe. Results were scrutinized by two other colleagues to ensure the validity of the search strategy and the findings. Any concerns regarding the eligibility of studies for inclusion were discussed among the research team. I then analyzed the co-occurrence of the key terms in the database. The resultant conceptual map is shown in Figure 19.4.

The position of a concept in a network—a study of “well-being.” Node size is proportional to the frequency of terms in 54 selected abstracts. Line thickness is proportional to the co-occurrence of two terms in any phrase of three words (e.g., subjective well-being, economics of well-being, well-being and development).

The diagram can be interpreted as a visualization of a conceptual space. So, when academics write about well-being in the context of public policy, they tend to connect the discussion to the other terms in the matrix. “Happiness,” “health,” “economic,” and “subjective,” for example, are relatively dominant terms in the matrix. The node size of these words suggests that references to such entities is only slightly less than references to well-being itself. However, when we come to analyze how well-being is talked about in detail, we see specific connections come to the fore. Thus, the data imply that talk of “subjective well-being” far outweighs discussion of “social well-being” or “economic well-being.” Happiness tends to act as an independent node (there is only one occurrence of happiness and well-being), probably suggesting that “happiness” is acting as a synonym for well-being. Quality of life is poorly represented in the abstracts, and its connection to most of the other concepts in the space is very weak—confirming, perhaps, that quality of life is unrelated to contemporary discussions of well-being and happiness. The existence of “measures” points to a distinct concern to assess and to quantify expressions of happiness, well-being, economic growth, and gross domestic product. More important and underlying this detail, there are grounds for suggesting that there are in fact a number of tensions in the literature on well-being.

On the one hand, the results point toward an understanding of well-being as a property of individuals—as something that they feel or experience. Such a discourse is reflected through the use of words like happiness, subjective , and individual . This individualistic and subjective frame has grown in influence over the past decade in particular, and one of the problems with it is that it tends toward a somewhat content-free conceptualization of well-being. To feel a sense of well-being, one merely states that one is in a state of well-being; to be happy, one merely proclaims that one is happy (cf., Office for National Statistics, 2012 ). It is reminiscent of the conditions portrayed in Aldous Huxley’s Brave New World , wherein the rulers of a closely managed society gave their priority to maintaining order and ensuring the happiness of the greatest number—in the absence of attention to justice or freedom of thought or any sense of duty and obligation to others, many of whom were systematically bred in “the hatchery” as slaves.

On the other hand, there is some intimation in our web that the notion of well-being cannot be captured entirely by reference to individuals alone and that there are other dimensions to the concept—that well-being is the outcome or product of, say, access to reasonable incomes, to safe environments, to “development,” and to health and welfare. It is a vision hinted at by the inclusion of those very terms in the network. These different concepts necessarily give rise to important differences concerning how well-being is identified and measured and therefore what policies are most likely to advance well-being. In the first kind of conceptualization, we might improve well-being merely by dispensing what Huxley referred to as “soma” (a superdrug that ensured feelings of happiness and elation); in the other case, however, we would need to invest in economic, human, and social capital as the infrastructure for well-being. In any event and even at this nascent level, we can see how CTA can begin to tease out conceptual complexities and theoretical positions in what is otherwise routine textual data.

Putting the Content of Documents in Their Place

I suggested in my introduction that CTA was a method of analysis—not a method of data collection or a form of research design. As such, it does not necessarily inveigle us into any specific forms of either design or data collection, though designs and methods that rely on quantification are dominant. In this closing section, however, I want to raise the issue as to how we should position a study of content in our research strategies as a whole. We must keep in mind that documents and records always exist in a context and that while what is “in” the document may be considered central, a good research plan can often encompass a variety of ways of looking at how content links to context. Hence, in what follows, I intend to outline how an analysis of content might be combined with other ways of looking at a record or text and even how the analysis of content might be positioned as secondary to an examination of a document or record. The discussion calls on a much broader analysis, as presented in Prior ( 2011 ).

I have already stated that basic forms of CTA can serve as an important point of departure for many types of data analysis—for example, as discourse analysis. Naturally, whenever “discourse” is invoked, there is at least some recognition of the notion that words might play a part in structuring the world rather than merely reporting on it or describing it (as is the case with the 2002 State of the Nation address that was quoted in the section “Units of Analysis”). Thus, for example, there is a considerable tradition within social studies of science and technology for examining the place of scientific rhetoric in structuring notions of “nature” and the position of human beings (especially as scientists) within nature (see, e.g., work by Bazerman, 1988 ; Gilbert & Mulkay, 1984 ; and Kay, 2000 ). Nevertheless, little, if any, of that scholarship situates documents as anything other than inert objects, either constructed by or waiting patiently to be activated by scientists.

However, in the tradition of the ethnomethodologists (Heritage, 1991 ) and some adherents of discourse analysis, it is also possible to argue that documents might be more fruitfully approached as a “topic” (Zimmerman & Pollner, 1971 ) rather than a “resource” (to be scanned for content), in which case the focus would be on the ways in which any given document came to assume its present content and structure. In the field of documentation, these latter approaches are akin to what Foucault ( 1970 ) might have called an “archaeology of documentation” and are well represented in studies of such things as how crime, suicide, and other statistics and associated official reports and policy documents are routinely generated. That, too, is a legitimate point of research focus, and it can often be worth examining the genesis of, say, suicide statistics or statistics about the prevalence of mental disorder in a community as well as using such statistics as a basis for statistical modeling.

Unfortunately, the distinction between topic and resource is not always easy to maintain—especially in the hurly-burly of doing empirical research (see, e.g., Prior, 2003 ). Putting an emphasis on “topic,” however, can open a further dimension of research that concerns the ways in which documents function in the everyday world. And, as I have already hinted, when we focus on function, it becomes apparent that documents serve not merely as containers of content but also very often as active agents in episodes of interaction and schemes of social organization. In this vein, one can begin to think of an ethnography of documentation. Therein, the key research questions revolve around the ways in which documents are used and integrated into specific kinds of organizational settings, as well as with how documents are exchanged and how they circulate within such settings. Clearly, documents carry content—words, images, plans, ideas, patterns, and so forth—but the manner in which such material is called on and manipulated, and the way in which it functions, cannot be determined (though it may be constrained) by an analysis of content. Thus, Harper’s ( 1998 ) study of the use of economic reports inside the International Monetary Fund provides various examples of how “reports” can function to both differentiate and cohere work groups. In the same way. Henderson ( 1995 ) illustrated how engineering sketches and drawings can serve as what she calls conscription devices on the workshop floor.

Documents constitute a form of what Latour ( 1986 ) would refer to as “immutable mobiles,” and with an eye on the mobility of documents, it is worth noting an emerging interest in histories of knowledge that seek to examine how the same documents have been received and absorbed quite differently by different cultural networks (see, e.g., Burke, 2000 ). A parallel concern has arisen with regard to the newly emergent “geographies of knowledge” (see, e.g., Livingstone, 2005 ). In the history of science, there has also been an expressed interest in the biography of scientific objects (Latour, 1987 , p. 262) or of “epistemic things” (Rheinberger, 2000 )—tracing the history of objects independent of the “inventors” and “discoverers” to which such objects are conventionally attached. It is an approach that could be easily extended to the study of documents and is partly reflected in the earlier discussion concerning the meaning of the concept of well-being. Note how in all these cases a key consideration is how words and documents as “things” circulate and translate from one culture to another; issues of content are secondary.

Studying how documents are used and how they circulate can constitute an important area of research in its own right. Yet even those who focus on document use can be overly anthropocentric and subsequently overemphasize the potency of human action in relation to written text. In that light, it is interesting to consider ways in which we might reverse that emphasis and instead to study the potency of text and the manner in which documents can influence organizational activities as well as reflect them. Thus, Dorothy Winsor ( 1999 ), for example, examined the ways in which work orders drafted by engineers not only shape and fashion the practices and activities of engineering technicians but also construct “two different worlds” on the workshop floor.

In light of this, I will suggest a typology (Table 19.3 ) of the ways in which documents have come to be and can be considered in social research.

While accepting that no form of categorical classification can capture the inherent fluidity of the world, its actors, and its objects, Table 19.3 aims to offer some understanding of the various ways in which documents have been dealt with by social researchers. Thus, approaches that fit into Cell 1 have been dominant in the history of social science generally. Therein, documents (especially as text) have been analyzed and coded for what they contain in the way of descriptions, reports, images, representations, and accounts. In short, they have been scoured for evidence. Data analysis strategies concentrate almost entirely on what is in the “text” (via various forms of CTA). This emphasis on content is carried over into Cell 2–type approaches, with the key differences being that analysis is concerned with how document content comes into being. The attention here is usually on the conceptual architecture and sociotechnical procedures by means of which written reports, descriptions, statistical data, and so forth are generated. Various kinds of discourse analysis have been used to unravel the conceptual issues, while a focus on sociotechnical and rule-based procedures by means of which clinical, police, social work, and other forms of records and reports are constructed has been well represented in the work of ethnomethodologists (see Prior, 2011 ). In contrast, and in Cell 3, the research focus is on the ways in which documents are called on as a resource by various and different kinds of “user.” Here, concerns with document content or how a document has come into being are marginal, and the analysis concentrates on the relationship between specific documents and their use or recruitment by identifiable human actors for purposeful ends. I have pointed to some studies of the latter kind in earlier paragraphs (e.g., Henderson, 1995 ). Finally, the approaches that fit into Cell 4 also position content as secondary. The emphasis here is on how documents as “things” function in schemes of social activity and with how such things can drive, rather than be driven by, human actors. In short, the spotlight is on the vita activa of documentation, and I have provided numerous example of documents as actors in other publications (see Prior, 2003 , 2008 , 2011 ).

Content analysis was a method originally developed to analyze mass media “messages” in an age of radio and newspaper print, well before the digital age. Unfortunately, CTA struggles to break free of its origins and continues to be associated with the quantitative analysis of “communication.” Yet, as I have argued, there is no rational reason why its use must be restricted to such a narrow field, because it can be used to analyze printed text and interview data (as well as other forms of inscription) in various settings. What it cannot overcome is the fact that it is a method of analysis and not a method of data collection. However, as I have shown, it is an analytical strategy that can be integrated into a variety of research designs and approaches—cross-sectional and longitudinal survey designs, ethnography and other forms of qualitative design, and secondary analysis of preexisting data sets. Even as a method of analysis, it is flexible and can be used either independent of other methods or in conjunction with them. As we have seen, it is easily merged with various forms of discourse analysis and can be used as an exploratory method or as a means of verification. Above all, perhaps, it crosses the divide between “quantitative” and “qualitative” modes of inquiry in social research and offers a new dimension to the meaning of mixed methods research. I recommend it.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

Publisher’s Note

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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• Open access
• Published: 18 April 2024

Integrating training in evidence-based medicine and shared decision-making: a qualitative study of junior doctors and consultants

• Mary Simons   ORCID: orcid.org/0000-0001-9627-7861 1 , 4 ,
• Georgia Fisher   ORCID: orcid.org/0000-0002-7252-7800 1 ,
• Samantha Spanos   ORCID: orcid.org/0000-0003-3734-3907 1 ,
• Yvonne Zurynski   ORCID: orcid.org/0000-0001-7744-8717 1 ,
• Andrew Davidson   ORCID: orcid.org/0000-0001-8449-3727 2 ,
• Marcus Stoodley   ORCID: orcid.org/0000-0002-4207-8493 3 ,
• Frances Rapport   ORCID: orcid.org/0000-0002-4428-2826 1 &
• Louise A. Ellis   ORCID: orcid.org/0000-0001-6902-4578 1  

BMC Medical Education volume  24 , Article number:  418 ( 2024 ) Cite this article

Metrics details

In the past, evidence-based medicine (EBM) and shared decision-making (SDM) have been taught separately in health sciences and medical education. However, recognition is increasing of the importance of EBM training that includes SDM, whereby practitioners incorporate all steps of EBM, including person-centered decision-making using SDM. However, there are few empirical investigations into the benefits of training that integrates EBM and SDM (EBM-SDM) for junior doctors, and their influencing factors. This study aimed to explore how integrated EBM-SDM training can influence junior doctors’ attitudes to and practice of EBM and SDM; to identify the barriers and facilitators associated with junior doctors’ EBM-SDM learning and practice; and to examine how supervising consultants’ attitudes and authority impact on junior doctors’ opportunities for EBM-SDM learning and practice.

We developed and ran a series of EBM-SDM courses for junior doctors within a private healthcare setting with protected time for educational activities. Using an emergent qualitative design, we first conducted pre- and post-course semi-structured interviews with 12 junior doctors and thematically analysed the influence of an EBM-SDM course on their attitudes and practice of both EBM and SDM, and the barriers and facilitators to the integrated learning and practice of EBM and SDM. Based on the responses of junior doctors, we then conducted interviews with ten of their supervising consultants and used a second thematic analysis to understand the influence of consultants on junior doctors’ EBM-SDM learning and practice.

Junior doctors appreciated EBM-SDM training that involved patient participation. After the training course, they intended to improve their skills in person-centered decision-making including SDM. However, junior doctors identified medical hierarchy, time factors, and lack of prior training as barriers to the learning and practice of EBM-SDM, whilst the private healthcare setting with protected learning time and supportive consultants were considered facilitators. Consultants had mixed attitudes towards EBM and SDM and varied perceptions of the role of junior doctors in either practice, both of which influenced the practice of junior doctors.

Conclusions

These findings suggested that future medical education and research should include training that integrates EBM and SDM that acknowledges the complex environment in which this training must be put into practice, and considers strategies to overcome barriers to the implementation of EBM-SDM learning in practice.

Peer Review reports

The practice of evidence-based medicine (EBM) requires clinicians to incorporate their own expertise, the best research evidence, and patient preferences when making decisions about patient care [ 1 ]. Since its introduction, approaches to teaching EBM skills have focused on the use of critical appraisal to determine the highest level of evidence, largely overlooking clinician expertise and patient preferences [ 2 , 3 ] and disregarding the established central role of person-centered care and shared decision-making (SDM), where clinician and patient make care decisions together [ 4 ]. This disparate approach may be connected to the way that EBM has been traditionally taught during medical training, where education about person-centered care and SDM has occurred in a separate educational silo to EBM education [ 2 , 5 , 6 ]. In recent years, a potential solution has been proposed: teaching EBM and SDM together, where evidence is applied using SDM skills [ 7 , 8 ].

Some educators and practitioners have identified the potential benefit of incorporating the principals of SDM into EBM training, so that education centers on the patient as well as the evidence [ 9 , 10 ]. However, very few published studies provide empirical data on how this can be successfully done [ 8 , 11 ]. In an Australian study, researchers ran a single EBM-SDM workshop for medical and allied health student-clinicians [ 12 ], where SDM was introduced as part of the students’ compulsory EBM course. In this study, participants who underwent SDM training in addition to reading SDM material scored significantly higher on measures of ability, attitudes, and confidence in incorporating SDM into EBM when compared to participants who read SDM material alone. In a more recent study, researchers from the same institution conducted a half-day EBM-SDM workshop to train primary care practitioners in using SDM with EBM to improve decision-making for patient care [ 13 ]. In this study, pre- and post- workshop observations of doctors’ skills in SDM were assessed via recorded consultations and pre- and post- workshop attitude questionnaires. The results from this pilot found that participants had increased positive attitudes towards SDM and improved SDM skills immediately after the half-day workshop [ 13 ], though the focus of this training was limited to general practice-focused clinical scenarios, did not incorporate a study follow-up, and omitted qualitative participant feedback. More recently, a scoping review of 23 studies found that while there has been increasing recognition by educators of the interdependence between EBM and SDM, only a minority of included studies explicitly incorporated EBM and SDM into training content [ 8 ].

We previously conducted a series of EBM training courses for junior doctors during which they were taught to apply evidence using SDM skills, namely, an EBM-SDM course. We ran a pilot mixed-methods evaluation, which indicated that while there was a significant increase in positive attitudes towards EBM after the course, there were also several barriers and facilitators that influenced the potential uptake and practice of EBM and SDM [ 14 ]. This is unsurprising, given that EBM training for junior doctors is beset by reports of failure to translate new skills and attitudes into clinical practice [ 9 ] and SDM is slow to be taken up among doctors in general [ 15 , 16 ]. The EBM literature has identified that the main reasons given by junior doctors for not practising EBM included: lack of time to learn [ 17 , 18 ] or practice EBM [ 19 ], workplace culture [ 20 ], and lack of prior training [ 20 ]. Separate SDM literature has identified that barriers to the practice of SDM perceived by doctors, including junior doctors, included time constraints [ 21 ], low levels of patient health literacy [ 22 ], workplace culture [ 23 ], and no opportunities to learn and practice SDM during clinical practice [ 24 ]. However, there are few investigations of barriers to the joint practice of EBM and SDM following their integrated training. As such, there is a need for more comprehensive qualitative evaluations of the outcomes of integrated EBM and SDM training, as well as a more in-depth understanding of the barriers and facilitators to their implementation in clinical practice.

Despite positive attitudinal changes towards EBM-SDM after training [ 13 , 14 ], it is likely that specific barriers prevent the provision of EBM-SDM training and the translation of new skills into clinical practice. It is important to further understand the nature of these barriers so that the impact of EBM and SDM practice can be fully realised. We were interested in examining the private hospital setting, and specific benefits or barriers this setting could introduce. Also of interest was the composition of junior doctor and consultant participant cohorts where most participants were undertaking surgical specialties or training, and its impact on influencing their responses and outcomes following training. In this study, we conducted interviews with junior doctors both before and after EBM-SDM training, and with their supervising consultants to further understand their perceptions and practice of EBM and SDM, and the associated barriers and facilitators.

This study aimed to answer the following research questions:

How does an integrated EBM-SDM course influence junior doctors’ attitudes toward, and practice of, EBM and SDM?

What are the barriers to junior doctors’ EBM-SDM learning and practice? What are the facilitators?

How do supervising consultants’ attitudes and influence impact on junior doctors’ opportunities for EBM-SDM learning and practice?

This study used an emergent qualitative design where data were collected via semi-structured interviews [ 25 ]. Social constructivist theory underpinned our study design to enable the exploration of how junior doctors and consultants created their own meanings, attitudes, and understanding about EBM and SDM, and a deeper understanding of their relationships with each other within this context [ 26 ]. The study centered around an EBM-SDM course that we conducted at an academic health sciences center. Phase 1 of this study involved conducting and analysing pre- and post-course interviews with junior doctors to understand their perceived barriers and facilitators to learning and practising EBM-SDM [ 27 ]. Thematic analysis of the initial interviews with junior doctors raised questions about the role of supervising consultant doctors in EBM-SDM learning and practice, specifically in terms of their support for training and practice opportunities for junior doctors. Thus, Phase 2 of the study used semi-structured interviews with consultants to further understand how their attitudes and influence might impact junior doctors’ opportunities for EBM-SDM learning and practice.

Study setting

The EBM-SDM training course took place at an integrated academic health sciences center (MQ Health) on an urban university campus, comprising a university-owned private hospital and specialty outpatient clinics [ 28 ]. The course was attended by junior doctors who worked at the center. In the Australian setting, junior doctors include new graduates or interns, residents undertaking prevocational training, registrars who are either accredited with a specialty training program or unaccredited, and fellows who have completed specialty training and are seeking sub-specialty training [ 29 ]. The EBM-SDM training course consisted of four 90-minute meetings, and covered all steps of the EBM process and the principles of SDM that are incorporated into the fourth EBM step. The course was conducted over an eight-week period to provide trainees with sufficient time in between meetings for reading, reviewing, and preparing material. The course was conducted five times during this study. Adult learning theory was used as a framework for the problem-based, collaborative learning environment where the teachers facilitated rather than directed learners [ 30 ]. During the course, junior doctors used their own patient cases to increase the course relevance to their practice and patient care [ 31 ]. Additional File 1 contains details of the structure and content of the EBM-SDM training course.

The junior doctors were on a single-term rotation, where they spent one year at the private hospital before returning to rotations in the public hospital system. They worked alongside a variety of other healthcare professionals, including consultants, allied health professionals, researchers, and educators, and were supervised by consultants, specialists from a range of medical and surgical disciplines, who provided individualised mentoring, opportunities for learning and research, and support to enter specialist training programs in Australia. Junior doctors could also take part in educational activities outside of their supervision with consultants, including the EBM-SDM course, to acquire and practice new skills.

Participant recruitment

Participants were recruited via purposive sampling [ 32 ] where doctors from a range of age groups and training backgrounds were approached to obtain a comprehensive sample. In Phase 1, participants were recruited from the university hospital’s training program for junior doctors. Using examples from the literature [ 33 ], an estimated number of 12 to 15 interviewees from the available pool of 30 junior doctors was considered appropriate to provide in-depth data, and to cover all the issues that could arise from interviews pre- and post- EBM-SDM training [ 32 ]. In a similar process, for Phase 2 we sought a sample of 10 consultants from the available pool of 20 who had current supervisory roles in the training of junior doctors at MQ Health. The junior doctors were approached as they enrolled in the EBM course, while the consultants were identified from a list of junior doctors’ supervisors provided by the faculty learning and teaching administration team and were sent individual emails inviting them to take part in the study.

Data collection

Demographic data.

A demographic survey was developed by four authors (MSi, FR, YZ, AD) and emailed to all consenting participants to record their age group, gender, position, country of medical training, period in which training occurred, and prior education in EBM and SDM.

Interview schedules

Interview questions were developed by the first author (MSi), then reviewed and amended with members of the author team (AD, FR, YZ). In Phase 1, two interview schedules were developed: pre-course and post-course. The pre-course interviews were designed to establish a pre-intervention baseline and explore how junior doctors understood and used both EBM and SDM, and their prior training experiences in each. The post-course interview questions examined changes in knowledge, attitudes, and practice of EBM and SDM and explored junior doctors’ perceptions of combined EBM-SDM training for learning and practice, their intentions to use knowledge gained, the influence of their supervising consultants on EBM and SDM practice, and possible barriers and facilitators to learning and using EBM. In Phase 2, interviews with consultants were designed to understand how they viewed EBM and SDM in their own practice, and their views on whether junior doctors should practice EBM and SDM. Interview questions also explored consultants’ views and experiences of combined EBM and SDM training, in influencing both clinical practice and medical education. See Additional File 2 for all interview schedules.

Interview pilot and sessions

In Phase 1, interview questions were designed and piloted with three junior doctors and were subsequently refined into the final interview schedules. In Phase 2, interviews were piloted with one consultant, after which the questions were modified for use with this cohort. Interviews took place in quiet locations with each junior doctor from 2019 until 2022, and with each consultant during 2021; they were conducted face-to-face in 2019, and via Zoom from 2020 due to the COVID-19 pandemic [ 34 ]. Author MSi conducted the interviews as 40-minute sessions. All interviewees were given the option to comment on their interview transcripts and study results. One interviewee returned for a second interview to capture additional data. Observational notes were taken by MSi to capture additional contextual factors (such as tone of voice) to assist with thematic analysis.

Data analysis

In Phase 1 of the study, junior doctors’ transcripts and field notes were thematically analysed [ 35 , 36 ] to identify, evaluate and report patterns or themes within the data in relation to the three research questions. The first author (MSi) transcribed and familiarised herself with the data. Iterative generation of codes and themes took place with other members of the authorship team (FR, YZ, LAE, GF, SS). Themes were inductively defined as new codes were generated and all themes and sub-themes were named. Transcripts were re-read, and themes reinterpreted until the team decided that data findings had been accurately described. These themes were then used in Phase 2 of the study as a framework to deductively analyse consultants’ interviews. We also included an ‘Other’ category to code any content that did not fit within the framework, and then inductively analysed this content to capture additional sub-themes from the consultant data.

Research team and reflexivity

MSi, a higher degree research student, developed and delivered the EBM-SDM training course with two other authors (MSt, AD). MSi also developed the interview schedules (with FR, AD, YZ) and conducted the interviews. All participants were informed of MSi’s involvement in the study. MSi has training qualifications in adult education and qualitative research methods, including group and individual interviewing techniques. She analysed the interview data with other authors (FR, YZ). MSi knew all study participants (except two consultants) through her work as a clinical librarian at Macquarie University and discussed with the other authors how her involvement in the study and familiarity with the participants may influence her perceptions and analysis of the interview data. FR, YZ, and SS are health service researchers, with extensive experience in qualitative research. As non-clinicians, they reflected on their experiences and expectations as patients, and as researchers, and how that may influence their interpretation of the interview data. GF and LAE are allied healthcare professionals by background and researchers who drew on their clinical and research skills and perspectives to interpret the interview data. AD and MSt are neurosurgeons with experience in training junior doctors and an interest in medical education and teaching EBM. They knew several study participants through their clinical and research work.

Ethical approval and study reporting

Ethics approval was obtained in 2019 to interview junior doctors from Macquarie University Human Research Ethics Committee (# 5201927419929), and in 2021 to add interviews with consultants (Ethics no: 52021274125020). The study was reported using COREQ guidelines (See Related Files).

Demographic information of participants

Demographic details of the junior doctors and consultants who participated in interviews are displayed in Table  1 . Of the 30 junior doctors who completed the EBM-SDM training, 12 participated in interviews. Of the 12 participating junior doctors, five were fellows, five were registrars, one was a resident, and one was an intern, and thus the junior doctor cohort represented a range of training levels and experience. Half of the junior doctors undertook their medical training in Australia and around two-thirds had some prior EBM instruction, although none had received training in SDM. Five junior doctors completed both pre and post interviews; those who only completed one interview cited time factors and clinical schedules as reasons for non-completion. Most junior doctors who completed the EBM-SDM training course but not the interviews cited time factors as reasons for their non-participation.

Ten consultants participated in interviews. Of these 10 consultants, three were Associate Professors and four were Professors. Five consultants had some prior EBM training, and none had any prior SDM training.

Themes and sub-themes

The study had three key research questions, and four major themes were identified around those questions. The themes, sub-themes, and links to the research questions are summarised in Table  2 . In the following results section, junior doctors’ quotes are indicated with “J” and a number; consultants’ quotes are indicated with “C” and a number.

Theme 1: EBM training, understanding, and practice

Four sub-themes were identified that related to perceptions and understanding of EBM training and practice: pre-course understanding and learning EBM, application to practice, training needs of junior doctors, and impact of medical speciality.

Understanding and training in EBM

Prior to the EBM-SDM course, most junior doctors equated EBM to research skills and knowledge-gain, e.g., “[EBM] …means medicine that has a foundation in scientific studies that have been rigorously peer reviewed and developed through a scientific method…” (J3). Some junior doctors linked EBM to a statistical outcome or risk measure, using it to give “ the risks of certain procedures … [and] the risks of conservative management versus operative management” (J4). Of the six junior doctors that trained in Australia, none recalled EBM training within a clinical setting or taught in a way that directly applied to practice. Instead, they reported that EBM training consisted of isolated lectures or projects: “but other than that, there was no course for EBM. It’s just lectures when I was in med[ical] school” (J5).

Five consultants indicated a lack of understanding of EBM practice when asked to prioritise its components: “ Literature-based EBM is the most important, anecdotal or doctors’ experiences is the least important, and what was the third one?” (C7), whilst others were more aware of EBM theory and practice, particularly as it applied to patient care: “ evidence-based medicine in its foundations is meant to tailor it to the particular patient and it is actually quite flexible” (C1).

Actual and intended practice of EBM

Junior doctors’ understanding of the practice of EBM broadened after the EBM-SDM course and was accompanied by increased acknowledgement of patient involvement in their care. One junior doctor described their increased awareness for future practice: “[the course made me wonder] how can I convey the message to patients and get them to be involved in deciding the management plan?” (J5). The greatest barrier to practising EBM was lack of time for learning and practice, with all junior doctors mentioning this during their interviews.

Training needs of junior doctors

Prior to the EBM-SDM training course, most junior doctors were looking forward to developing skills in searching and critically appraising evidence: “ I’d like a better understanding of what a good quality study is…if something is a RCT or cohort study that I want to be able to say, this is a good RCT or, this is a good cohort study” (J4). After the EBM-SDM training course, several junior doctors recommended further training to help them maintain and extend their skills. Some suggested EBM training should be provided for longer and include refresher training, and one suggested giving more emphasis to the SDM component “ because this is the practical part of putting it into our daily life, applying it to patients” (J5).

Impact of the medical speciality of consultants

Consultants’ specialisations impacted their practice of EBM. Those practising as physicians, including a neurologist and cardiologist, reported greater access to high-level evidence and guidelines, with one consultant claiming that “ cardiology is very algorithmic in a lot of ways, and that makes that easier…there’s only so many things you can do…. that kind of distils things” (C6). Consultants from surgical disciplines reported that lower levels of evidence were often drawn upon for decision-making, because “[in surgery] the evidence, sometimes is not like hard science…many times we base our decisions on grey literature, or on evidence that we acquire over time…or from the experience of our other senior colleagues” (C9).

Theme 2: attitudes towards EBM

Three sub-themes were interpreted within the data relating to attitudes towards EBM: attitudes towards the role of evidence in decision-making, attitudes towards patient involvement in care decisions, and attitudes towards junior doctors’ practice of EBM.

Attitudes towards the role of evidence in decision-making

Prior to the EBM-SDM training course, most junior doctors’ attitudes toward EBM were focused on the knowledge they could acquire for decision-making, research, and benchmarking their performance, such as “ recommendations that are based on that evidence to inform medical decision-making” (J3). After the course junior doctors were keen to practice their new EBM skills that had expanded to include finding and using evidence to explain care issues to patients. “It [explaining evidence] really makes them [patients] feel as though they’re being actively involved in the actual details of their specific case” (J3).

Consultant participants frequently discussed the pitfalls of using evidence to inform decisions, with one claiming that “[EBM has] got enormous weaknesses if people think that there’s evidence for everything; that is too simplistic and left brain” (C2). Furthermore, decisions were reportedly often informed by “ what you’ve been taught by your people training you and your mentors” (C5). Two consultants explained how they perceived EBM was negatively changing medical practice: “ [EBM] takes away some of the enjoyment out of practicing medicine individually, in the sense that some of the art has been lost” (T7). Other consultants pointed out advantages of EBM, including provision of high-quality evidence for decision-making that “gives me the ability to then converse with patients as to why we do things and why it would be most appropriate” (C1). Two consultants with prior EBM training discussed the conflict with senior colleagues that can often arise when EBM is practised, one stating that “ sometimes this evidence is not strong enough to change the opinion of some [senior] doctors or surgeons” (C9).

Attitudes towards patient involvement in care decisions

Junior doctors expressed mixed attitudes about patient involvement in decisions. Despite post-training beliefs that patient involvement “ will help to establish…better rapport with patients…because they’re more informed and there’s more trust” (J3), junior doctors also reported the “ need to simplify things for the patient who makes the decision about their life… other than just giving information” (J8). Six junior doctors did, however, plan for greater patient involvement after they completed the EBM-SDM course: “ I am now more inclined to include evidence-based discussions…in how I approach decisions that we present to patients…. I wouldn’t have really brought it up as a topic [previously]” (J3).

Consultants also reported mixed attitudes to patient involvement in their care, with one participant stating that “ it’s good that they’re enthusiastic about it but it’s bad that it’s this sort of modern attitude of ‘my opinion’s as good as your opinion’, even if my opinion is based on social media and newspaper reports” (C4). Six consultants expressed doubts about patients’ ability to grasp complex medical concepts for decision-making, to “ understand something as much as a clinician who’s been doing it for 10, 20, 30 years” (C8). Three consultants strongly endorsed patient involvement, mostly believing that “ at the end of the day … it’s the patient’s body, that they have to be comfortable with the treatment plan” (C1).

Consultant attitudes towards junior doctors’ practice of EBM

Consultants differed in their opinions on whether junior doctors should practice EBM. Five consultants believed there were few roles for junior doctors in evidence-based decision-making, one stating: “ they practice a very protocol driven medicine. And that’s just historical and that’s probably not a bad thing” (C2). The other five consultants, in contrast, stated that limited decision-making roles should exist for junior doctors: “ doctors at any stage should be able to assess the patient and so they can influence decision-making, based on that ” (C3).

Theme 3. Organisational culture and EBM

Two sub-themes were identified pertaining to the influence of organisational culture on practicing EBM: public versus private healthcare, and medical hierarchy.

Public vs. private healthcare

Junior doctors and consultants spoke of differences in EBM learning and practice between public and private healthcare settings. Six junior doctors reported that private healthcare settings, such as the academic health sciences center they were based in, facilitated the practice of EBM, because they had protected time for individual study and educational activities. This did not happen during their public hospital rotations, where junior doctors cited high patient numbers and associated workloads that were prioritised. One such junior doctor stated “ Today I’ve just been allocated a study day… I don’t actually think that happens in public hospitals” ( J4 ) .

Four consultants’ views aligned with those of junior doctors about greater protected time available for learning in private settings. Three consultants stated junior doctors had greater opportunities for patient decision-making in the public system, for example, in the emergency department of public hospitals where “ you see people who are coming in [to the emergency department] and often they’ll see the junior doctors before they even see the senior doctor ” (C6).

Medical hierarchy

Junior doctors and some consultants discussed the emphasis placed on following the instructions of the most senior consultants. Six junior doctors reported that they were rarely involved in decision-making, but rather, follow the consultant’s lead, regardless of whether the consultant’s decisions were evidence driven. Prior to the EBM-SDM course one junior doctor stated: “ I think in some of my other terms, if I had asked, they [consultants] would just say “this is just part of my experience” (J2). She maintained this view after the course, recalling one instance when querying a guideline put in place by a consultant: “ I know as a junior sometimes you get a bit of pushback if what you’re recommending is not guideline driven” (J2).

Two consultants reported that their decision-making capacity was also restricted by their senior colleagues, one consultant claiming that this was “the consequence of the traditional school and all the experience, based on the decades of “we always did it like that” (C9). Another consultant spoke of the difficulties faced by those consultants who completed their medical training before EBM was introduced:

If you look at some of the older clinicians you can be forgiven for thinking that they’re kind of stuck in, frozen in time, right? And that might be a generational thing, but because of this new focus on evidence-based learning and medicine in the nineties, these clinicians didn’t have the benefit of that. (C3.)

Three junior doctors reported that hierarchies were evident even among themselves, and not just between junior doctors and consultants, such that accredited registrars or fellows often held greater credibility than less experienced residents, interns, and unaccredited registrars. Two consultants stated that they only worked with fellows, not the more junior ranked doctors, whereas other consultants reported greater inclusivity of all junior doctors during decision-making, one stating: “ I am very, very open to accept the data or opinion [of a junior doctor] because it’s based on something which is more updated than what I know, and this is something that happens” (C9).

Theme 4: understanding and practice of SDM and its role in EBM

Three sub-themes were identified relating to the understanding and practice of SDM and its role in EBM: Understanding and practicing SDM, the effect of hierarchy on the practice of person-centered care and SDM, and the role of junior doctors in the learning and practice of SDM.

Understanding and practicing SDM

Prior to the EBM-SDM course, four junior doctors could not correctly define what SDM meant, and six described SDM as one-way communication of evidence to patients. After the course, they claimed a greater understanding of SDM as part of person-centered care, and that “ you need to have a good basis in EBM, to actually make sure the patient can be even involved in the discussion. So, the patient understands” (J4). Seven junior doctors believed that SDM and EBM should be taught together, whereas one did not agree: “ I think we don’t need to explicitly incorporate it, that it’s a given” (J1). Given that the training level of junior doctors was highly varied (i.e., from intern to fellow), there was variability in how they understood and approached SDM. For example, fellows, the most experienced of the junior doctors, described using evidence to provide recommendations to patients rather than eliciting patient preferences whilst referring to evidence. One fellow stated: “I think most patients are really welcoming if you tell them that people have done it before, the percentage of people who do good, for example, and those that don’t and they’re willing to accept that” (J10). Consultants conveyed mixed definitions of SDM; some saw it as informed consent, and others saw it as the transfer of information from doctor to patient. All consultants pointed out the difficulties of SDM, with one highlighting that “ it’s really hard to get somebody to the level where they can make some sort of an educated decision” (C8). One consultant commented on the differences in attitudes towards SDM between older and younger colleagues: “ younger clinicians are less likely to be as paternalistic [than older consultants], they’re more willing to accept that patients have their own thoughts, even if they’re unconventional and unrealistic” (C3). Surgeons and surgical trainees, comprising 72% of the study cohort, tended to view EBM and SDM as doctor-driven rather than patient-centered. For example, one neurosurgeon emphasised the important sources of evidence used for patient decisions: “So I always bring to the patient my experience, I bring the MDT [Multidisciplinary Team] meeting decision … and the literature” (C9). This contrasted with the perspective of non-surgical consultants. For example, a cardiologist highlighted the central role of the patient in the decision-making process: “I always think of evidence as the hard science and then for the decision-making process, about the application of that hard science to a particular context and … it’s in that paradigm, that the patient’s point of view is used to temper the evidence that you’re presenting” (C6).

Effect of medical hierarchy on junior doctors’ practice of person-centered care and SDM

Six junior doctors reported that, due to their place in the medical hierarchy, they tended not to practice SDM. One participant stated:

I actually try to hold off on doing that [practising SDM], personally, just because it’s more of a consultant discussion at that stage. When a consultant leaves the room, the patient does actually have more questions, and sometimes I just reiterate what the consultant has already said. (J4.)

Ten junior doctors planned to increase their communication and person-centered care skills after the EBM-SDM course, for example, using EBM to find evidence that reassures a patient; skills that could be implemented now and expanded later to incorporate SDM.

Consultant perceptions of the role of junior doctors in SDM

Four consultants were of the view that junior doctors should not practice SDM due to their junior level. One consultant reported that junior doctors sometimes played a patient advocate role because they “ often have an insight into some of those other levels [of patient care]” (C2). Another consultant considered providing junior doctors “the opportunity to be more involved in that [SDM] discussion” (C7) but cited time constraints as a barrier.

This study explored how integrated EBM and SDM training can impact attitudes, understanding and practice among junior doctors, and whether the attitudes and practice of their supervising consultants can influence those outcomes. Junior doctors demonstrated significant positive attitude changes towards EBM and SDM after the EBM-SDM course. Prior EBM training (during medical training or afterwards) was mostly didactic and focused on knowledge and skill acquisition which is a common finding in other studies that has not equipped junior doctors to practice EBM confidently in clinical settings [ 37 , 38 ]. Following our EBM-SDM course, not only did junior doctors’ knowledge and skills improve, but they frequently referred to the benefits of including patients in their discussions about care, which indicated that they had expanded their understanding of EBM to incorporate aspects of person-centered care. Their intentions to be more person-centered were frequently based on using evidence to effectively communicate risks and benefits to patients, rather than having SDM conversations with patients where all options were described, and decisions made together. However, there appeared to be a disconnect between the practice of SDM and the recognition of its practice. On several occasions, junior doctors facilitated SDM by answering patient questions after the consultant left the room, or by reiterating what the consultant said, but failed to recognise this as part of a SDM conversation with the patient.

Junior doctors also varied in their attitudes and practices of SDM. The more experienced junior doctors, the five fellows, tended to demonstrate a more doctor-centered rather than patient-centered approach to patient care than the less experienced junior doctors (i.e., residents). Junior doctors were at varying levels of their medical training, some of them closer to consultant-level practitioners than others, and may perceive and think about SDM differently depending on their training cohort. Furthermore, several fellows had worked as consultants in their home countries which may have influenced the doctor-centered patterns of decision-making commonly found among consultants. Thus, our study identified that junior doctors attitudes and practices of SDM are likely due to a lack of specific knowledge and understanding of SDM, limited prior training, as well as cultural conventions that may be associated with time and country of training.

Consultants varied greatly in their understanding of EBM and SDM, and their views on whether either should be practised by junior doctors. Senior consultants who completed medical training before the formal introduction of EBM in the 1990s [ 39 ] appeared to be unfamiliar with and less accepting of EBM and SDM and expressed a reluctance for junior doctors to engage in either. In contrast, younger consultants who had prior exposure to EBM training and practice tended to appreciate the benefits of EBM for junior doctors and patients. In another study of junior doctors and senior anaesthetists, interviews indicated there was a link between career stage and workplace settings and EBM attitudes [ 40 ]. In this study, senior anaesthetists (consultants) were reluctant to make decisions or change practice based on evidence in preference to their own experience and opinion [ 40 ]. Junior doctors regarded this as reluctance to change as due to older age, but the consultants saw it as surrendering their professional autonomy [ 40 ]. Thus, there may be a tendency among more senior doctors to resist practising EBM in favour of using their own decision-making preferences, that carry a risk of cognitive bias and are potentially suboptimal or obsolete decisions [ 40 , 41 , 42 ]. In addition, some studies have shown senior medical staff (consultants) have very little expertise in SDM with patients, thereby failing to become the role models in EBM-SDM that junior doctors need [ 43 ]. Senior doctors have also reported difficulty in using technology thus preferring to ask colleagues for advice [ 44 ].

In our study, more senior consultants appeared to dominate the medical workforce hierarchy and exclude junior doctors and patients from decision-making. These consultants believed that decision-making should be underpinned by their experience, knowledge, and their communities of practice. Thus, they did not prioritise decision-making linked to EBM and SDM and consequently educational opportunities for junior doctors under their supervision were reduced. These findings support those of other studies concerning the impact of medical hierarchies on junior medical staff, where power is recognised to sit with senior medical staff positioned at the top of the hierarchy, thereby reducing the autonomy of those positioned lower in the hierarchy, such as junior doctors [ 40 , 45 ]. This has been reported to be particularly evident in surgical specialties, where decision-making is dominated by senior surgeons’ experience rather than evidence [ 46 ]. Junior doctors learn to respect hierarchy from medical school, where they do not challenge authority to avoid unwanted impacts on their training and career progression [ 47 , 48 , 49 ]. The well-established medical hierarchy emerged as a barrier preventing junior doctors in our study from using evidence-based decision-making skills learned in the EBM-SDM course, particularly if the evidence contradicted strongly held views and practices of senior consultants.

Of note was that the present study was conducted during the COVID-19 pandemic, a difficult and uncertain time for all medical professionals. In the Australian context, junior doctors have reported restrictive workplace cultures and behaviours, including being overlooked and undervalued by senior doctors, which contributed negatively to their psychological well-being during COVID-19 [ 49 ]. This had important implications for doctors’ welfare, workforce retention, and safe patient care that needed to be addressed through “positive workplace cultural interventions to engage, validate and empower junior doctors” [ 50 ]. In contrast, junior doctors in our study, and in others, have reported that many consultants and senior medical staff were always supportive and approachable role models, not just during the pandemic, and helped to facilitate their trainees’ well-being and progress [ 47 , 51 ]. The potential contribution of such role models to facilitate and support EBM and SDM learning and practice may help to overcome some of the associated barriers [ 52 ].

Combining EBM and SDM training enabled junior doctors to realise there is more to EBM than the level of evidence, which was what most believed before the training. The combined course enabled them to consider how they would communicate the relevant evidence in a two-way conversation with the patient, and thus situated the principles of EBM within the broader context of patient needs and preferences. Several junior doctors had commented that their awareness and practice of improved communication skills with patients had increased after the course, lending support to the effectiveness of the combined course, and the likelihood that the learnings would be utilised in future. These outcomes also imply that EBM-SDM training has the potential to shift power dynamics within the medical hierarchy through expanding the skillset and abilities of junior doctors.

Another facilitator of combined EBM-SDM learning and practice reported in our study was the capacity of private healthcare facilities in Australia to provide protected time for educational activities. This contrasted with public healthcare facilities, where such opportunities are limited [ 53 ]. Our study took place within a neurosurgery department where a half-day is set aside each week for learning and teaching meetings, including the EBM-SDM course. The meetings were co-ordinated by consultants, thereby enabling junior doctors to learn and practice new skills with consultants’ support. In a similar way, consultants who recognise the benefits of EBM and SDM could act as unofficial champions, who provide further learning and teaching opportunities for junior doctors, whilst demonstrating and communicating those benefits to their senior colleagues. The idea of champions comes from literature demonstrating that colleagues or supervisors of junior clinicians can be a great source of assistance and support when it comes to learning and practicing skills associated with EBM [ 8 ]. Such champions or role models have been recommended as an integral part of EBM teaching because they demonstrate to learners the ‘how-to’ of the application of EBM principles to clinical practice and individual patients [ 54 ]. Within our study, this supportive culture, led by a champion or role model, was very beneficial. One of the neurosurgeon consultants took a keen interest in teaching EBM to junior doctors and he led by example, showing them how to use it in daily practice through patient care consultations, and ward rounds and by leading the EBM-SDM teaching during protected education time. The junior doctors responded with increased motivation to practice their EBM-SDM skills during educational meetings. This opportunity provided by a private healthcare facility could be an exemplar of EBM-SDM education in the Australian context that may be adapted by other institutions.

Future directions

A lack of prior learning and practice of EBM and SDM concepts among this sample of junior doctors echoes previous calls for improved basic and ongoing training in EBM and SDM skills [ 8 , 55 ]. The recently updated Australian specialist training program [ 56 ] has cited the inclusion of EBM and SDM as separate skill sets, with an emphasis on skills and knowledge acquisition. However, there is now a framework providing core competencies that can underpin an EBM curriculum incorporating SDM [ 57 ]. This is a promising initiative that could be adapted and used to meet the needs of institutions whilst identifying and managing barriers and facilitators to the learning and practice of EBM and SDM. Additionally, the capacity of consultants with prior EBM training and experience to act as champions of EBM-SDM could be further explored.

Future research opportunities include evaluation of the impacts of integrated EBM-SDM training content and strategies to determine optimal approaches for educators to adopt in both private and public settings. Future research should also focus on the efficacy of strategies to empower junior doctors to become more independent in using their EBM and SDM skills, such as training champions and consultants who want to help their junior doctor trainees develop skills and experience in EBM and SDM [ 52 , 58 ]. Finally, further investigation is warranted into the significance of undertaking medical training either before or after the introduction of EBM in the 1990s, and how this impacts the medical hierarchy, EBM-SDM training and practice opportunities for junior doctors, and patient care. These investigations could incorporate other qualitative methods such as ethnography to fully capture perceived dynamics and cultural conventions within medical disciplines.

Strengths & limitations

This study has contributed to our knowledge of combined EBM-SDM training in the Australian context. A strength of the study was its emergent design, where consultant interviews in Phase 2 were added after data were analysed from junior doctor interviews in Phase 1. This approach enabled consultant interview schedules to further elucidate the barriers and facilitators associated with EBM and SDM learning and practice that emerged during Phase 1. The study was also strengthened by including two diverse, but linked participant groups, the junior doctors, and their supervising consultants, thus facilitating the collection and analysis of more than one source of relevant data that addressed the study aims. However, the study is not without its limitations. First, the modest sample size of the study, exacerbated by COVID-19 restrictions and the impact of the pandemic on the medical workforce, reduces the study’s transferability to other cohorts and contexts. Second, junior doctors’ limited understanding of SDM after the course may reflect a limitation of the course. Although SDM was introduced and discussed in the course, little time was provided for deliberate SDM practice and feedback; an issue that can be rectified in future training and research. Third, more males than females participated in the study which may have influenced the pattern of results and is an area for further research.

Most junior doctors reported positive attitude changes following EBM-SDM training that encompassed plans to increase patient involvement in their care through better communication and evidence-based shared decision-making. However, time constraints and the influence of the medical hierarchy were significant barriers for most junior doctors when learning and practising EBM and SDM. Despite these barriers, supportive consultants and protected educational time facilitated the learning and practice of EBM and SDM within the context of our study. To counter the reported barriers at our institution there are opportunities available for some consultants to become champions who make protected time available for EBM-SDM learning and practice opportunities. These findings may inform future research and training where integrated EBM and SDM learning and practice could be adapted to the unique contextual and cultural influences of each institution.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Evidence-based medicine
• Shared decision-making

Consolidated criteria for reporting qualitative research

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Acknowledgements

The authors wish to thank the doctors who participated in the interviews reported in this paper.

No funding was received for conducting this study.

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Mary Simons, Georgia Fisher, Samantha Spanos, Yvonne Zurynski, Frances Rapport & Louise A. Ellis

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All authors contributed to the study conception and design. Material preparation and data collection were performed by MSi. Thematic analysis was performed by MSi, FR, YZ, GF, SS and LAE. MSt and AD prepared manuscript tables. The first draft of the manuscript was written by MSi, SS and GF. All authors contributed to each version of the manuscript. All authors read and approved the final manuscript.

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Ethics approval was obtained in 2019 to interview junior doctors from Macquarie University Human Research Ethics Committee (Ethics no: 5201927419929), and in 2021 to add interviews with consultants (Ethics no: 52021274125020). Informed consent was obtained from all individual participants included in the study.

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Simons, M., Fisher, G., Spanos, S. et al. Integrating training in evidence-based medicine and shared decision-making: a qualitative study of junior doctors and consultants. BMC Med Educ 24 , 418 (2024). https://doi.org/10.1186/s12909-024-05409-y

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“So at least now I know how to deal with things myself, what I can do if it gets really bad again”—experiences with a long-term cross-sectoral advocacy care and case management for severe multiple sclerosis: a qualitative study

• Anne Müller   ORCID: orcid.org/0000-0002-2456-2492 1 ,
• Fabian Hebben   ORCID: orcid.org/0009-0003-6401-3433 1 ,
• Kim Dillen 1 ,
• Veronika Dunkl 1 ,
• Yasemin Goereci 2 ,
• Raymond Voltz 1 , 3 , 4 ,
• Peter Löcherbach 5 ,
• Clemens Warnke   ORCID: orcid.org/0000-0002-3510-9255 2 &
• Heidrun Golla   ORCID: orcid.org/0000-0002-4403-630X 1

on behalf of the COCOS-MS trial group represented by Martin Hellmich

BMC Health Services Research volume  24 , Article number:  453 ( 2024 ) Cite this article

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Metrics details

Persons with severe Multiple Sclerosis (PwsMS) face complex needs and daily limitations that make it challenging to receive optimal care. The implementation and coordination of health care, social services, and support in financial affairs can be particularly time consuming and burdensome for both PwsMS and caregivers. Care and case management (CCM) helps ensure optimal individual care as well as care at a higher-level. The goal of the current qualitative study was to determine the experiences of PwsMS, caregivers and health care specialists (HCSs) with the CCM.

In the current qualitative sub study, as part of a larger trial, in-depth semi-structured interviews with PwsMS, caregivers and HCSs who had been in contact with the CCM were conducted between 02/2022 and 01/2023. Data was transcribed, pseudonymized, tested for saturation and analyzed using structuring content analysis according to Kuckartz. Sociodemographic and interview characteristics were analyzed descriptively.

Thirteen PwsMS, 12 caregivers and 10 HCSs completed interviews. Main categories of CCM functions were derived deductively: (1) gatekeeper function, (2) broker function, (3) advocacy function, (4) outlook on CCM in standard care. Subcategories were then derived inductively from the interview material. 852 segments were coded. Participants appreciated the CCM as a continuous and objective contact person, a person of trust (92 codes), a competent source of information and advice (on MS) (68 codes) and comprehensive cross-insurance support (128 codes), relieving and supporting PwsMS, their caregivers and HCSs (67 codes).

Conclusions

Through the cross-sectoral continuous support in health-related, social, financial and everyday bureaucratic matters, the CCM provides comprehensive and overriding support and relief for PwsMS, caregivers and HCSs. This intervention bears the potential to be fine-tuned and applied to similar complex patient groups.

Trial registration

The study was approved by the Ethics Committee of the University of Cologne (#20–1436), registered at the German Register for Clinical Studies (DRKS00022771) and in accordance with the Declaration of Helsinki.

Peer Review reports

Introduction

Multiple sclerosis (MS) is the most frequent and incurable chronic inflammatory and degenerative disease of the central nervous system (CNS). Illness awareness and the number of specialized MS clinics have increased since the 1990s, paralleled by the increased availability of disease-modifying therapies [ 1 ]. There are attempts in the literature for the definition of severe MS [ 2 , 3 ]. These include a high EDSS (Expanded disability Status Scale [ 4 ]) of ≥ 6, which we took into account in our study. There are also other factors to consider, such as a highly active disease course with complex therapies that are associated with side effects. These persons are (still) less disabled, but may feel overwhelmed with regard to therapy, side effects and risk monitoring of therapies [ 5 , 6 ].

Persons with severe MS (PwsMS) develop individual disease trajectories marked by a spectrum of heterogeneous symptoms, functional limitations, and uncertainties [ 7 , 8 ] manifesting individually and unpredictably [ 9 ]. This variability can lead to irreversible physical and mental impairment culminating in complex needs and daily challenges, particularly for those with progressive and severe MS [ 5 , 10 , 11 ]. Such challenges span the spectrum from reorganizing biographical continuity and organizing care and everyday live, to monitoring disease-specific therapies and integrating palliative and hospice care [ 5 , 10 ]. Moreover, severe MS exerts a profound of social and economic impact [ 9 , 12 , 13 , 14 ]. PwsMS and their caregivers (defined in this manuscript as relatives or closely related individuals directly involved in patients’ care) often find themselves grappling with overwhelming challenges. The process of organizing and coordinating optimal care becomes demanding, as they contend with the perceived unmanageability of searching for, implementing and coordinating health care and social services [ 5 , 15 , 16 , 17 ].

Case management (CM) proved to have a positive effect on patients with neurological disorders and/or patients with palliative care needs [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. However, a focus on severe MS has been missed so far Case managers primarily function as: (1) gatekeeper involving the allocation of necessary and available resources to a case, ensuring the equitable distribution of resources; as (2) broker assisting clients in pursuing their interests, requiring negotiation to provide individualized assistance that aligns as closely as possible with individual needs and (3) advocate working to enhance clients’ individual autonomy, to advocate for essential care offers, and to identify gaps in care [ 25 , 26 , 27 , 28 , 29 ].

Difficulties in understanding, acting, and making decisions regarding health care-related aspects (health literacy) poses a significant challenge for 54% of the German population [ 30 ]. Additionally acting on a superordinate level as an overarching link, a care and case management (CCM) tries to reduce disintegration in the social and health care system [ 31 , 32 ]. Our hypothesis is that a CCM allows PwsMS and their caregivers to regain time and resources outside of disease management and to facilitate the recovery and establishment of biographical continuity that might be disrupted due to severe MS [ 33 , 34 ].

Health care specialists (HCSs) often perceive their work with numerous time and economic constraints, especially when treating complex and severely ill individuals like PwsMS and often have concerns about being blamed by patients when expectations could not be met [ 35 , 36 ]. Our hypothesis is that the CCM will help to reduce time constraints and free up resources for specialized tasks.

To the best of our knowledge there is no long-term cross-sectoral and outreaching authority or service dedicated to assisting in the organization and coordination of the complex care concerns of PwsMS within the framework of standard care addressing needs in health, social, financial, every day and bureaucratic aspects. While some studies have attempted to design and test care programs for persons with MS (PwMS), severely affected individuals were often not included [ 37 , 38 , 39 ]. They often remain overlooked by existing health and social care structures [ 5 , 9 , 15 ].

The COCOS-MS trial developed and applied a long-term cross-sectoral CCM intervention consisting of weekly telephone contacts and monthly re-assessments with PwsMS and caregivers, aiming to provide optimal care. Their problems, resources and (unmet) needs were assessed holistically including physical health, mental health, self-sufficiency and social situation and participation. Based on assessed (unmet) needs, individual care plans with individual actions and goals were developed and constantly adapted during the CCM intervention. Contacts with HCSs were established to ensure optimal care. The CCM intervention was structured through and documented in a CCM manual designed for the trial [ 40 , 41 ].

Our aim was to find out how PwsMS, caregivers and HCSs experienced the cross-sectoral long-term, outreaching patient advocacy CCM.

This study is part of a larger phase II, randomized, controlled clinical trial “Communication, Coordination and Security for people with severe Multiple Sclerosis (COCOS-MS)” [ 41 ]. This explorative clinical trial, employing a mixed-method design, incorporates a qualitative study component with PwsMS, caregivers and HCSs to enrich the findings of the quantitative data. This manuscript focuses on the qualitative data collected between February 2022 and January 2023, following the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [ 42 ].

Research team

Three trained authors AM, KD and FH (AM, female, research associate, M.A. degree in Rehabilitation Sciences; KD, female, researcher, Dr. rer. medic.; FH, male, research assistant, B.Sc. degree in Health Care Management), who had no prior relationship with patients, caregivers or HCSs conducted qualitative interviews. A research team, consisting of clinical experts and health services researchers, discussed the development of the interview guides and the finalized category system.

Theoretical framework

Interview data was analyzed with the structuring content analysis according to Kuckartz. This method enables a deductive structuring of interview material, as well as the integration of new aspects found in the interview material through the inductive addition of categories in an iterative analysis process [ 43 ].

Sociodemographic and interview characteristics were analyzed descriptively (mean, median, range, SD). PwsMS, caregivers and HCSs were contacted by the authors AM, KD or FH via telephone or e-mail after providing full written informed consent. Participants had the option to choose between online interviews conducted via the GoToMeeting 10.19.0® Software or face-to-face. Peasgood et al. (2023) found no significant differences in understanding questions, engagement or concentration between face-to-face and online interviews [ 44 , 45 ]. Digital assessments were familiar to participants due to pandemic-related adjustments within the trial.

Out of 14 PwsMS and 14 caregivers who were approached to participate in interviews, three declined to complete interviews, resulting in 13 PwsMS (5 male, 8 female) and 12 caregiver (7 male, 5 female) interviews, respectively (see Fig.  1 ). Thirty-one HCSs were contacted of whom ten (2 male, 8 female) agreed to be interviewed (see Fig.  2 ).

Flowchart of PwsMS and caregiver participation in the intervention group of the COCOS-MS trial. Patients could participate with and without a respective caregiver taking part in the trial. Therefore, number of caregivers does not correspond to patients. For detailed inclusion criteria see also Table  1 in Golla et al. [ 41 ]

Flowchart of HCSs interview participation

Setting and data collection

Interviews were carried out where participants preferred, e.g. at home, workplace, online, and no third person being present. In total, we conducted 35 interviews whereof 7 interviews face-to-face (3 PwsMS, 3 caregivers, 1 HCS).

The research team developed a topic guide which was meticulously discussed with research and clinical staff to enhance credibility. It included relevant aspects for the evaluation of the CCM (see Tables  1 and 2 , for detailed topic guides see Supplementary Material ). Patient and caregiver characteristics (covering age, sex, marital status, living situation, EDSS (patients only), subgroup) were collected during the first assessment of the COCOS-MS trial and HCSs characteristics (age, sex, profession) as well as interview information (length and setting) were collected during the interviews. The interview guides developed for this study addressed consistent aspects both for PwsMS and caregivers (see Supplementary Material ):

For HCSs it contained the following guides:

Probing questions were asked to get more specific and in-depth information. Interviews were carried out once and recorded using a recording device or the recording function of the GoToMeeting 10.19.0® Software. Data were pseudonymized (including sensitive information, such as personal names, dates of birth, or addresses), audio files were safely stored in a data protection folder. The interview duration ranged from 11 to 56 min (mean: 23.9 min, SD: 11.1 min). Interviews were continued until we found that data saturation was reached. Audio recordings were transcribed verbatim by an external source and not returned to participants.

Data analysis

Two coders (AM, FH) coded the interviews. Initially, the first author (AM) thoroughly reviewed the transcripts to gain a sense of the interview material. Using the topic guide and literature, she deductively developed a category system based on the primary functions of CM [ 25 , 26 , 27 , 28 , 29 ]. Three interviews were coded repeatedly for piloting, and inductive subcategories were added when new themes emerged in the interview material. This category system proved suitable for the interview material. The second coder (FH) familiarized himself with the interview material and category system. Both coders (AM, FH) independently coded all interviews, engaging in discussions and adjusting codes iteratively. The finalized category system was discussed and consolidated in a research workshop and within the COCOS-MS trial group and finally we reached an intercoder agreement of 90% between the two coders AM and FH, computed by the MAXQDA Standard 2022® software.

We analyzed sociodemographic and interview characteristics using IBM SPSS Statistics 27® and Excel 2016®. Transcripts were managed and analyzed using MAXQDA Standard 2022®.

Participants were provided with oral and written information about the trial and gave written informed consent. Ethical approvals were obtained from the Ethics Committee of the University of Cologne (#20–1436). The trial is registered in the German Register for Clinical Studies (DRKS) (DRKS00022771) and is conducted under the Declaration of Helsinki.

Characteristics of participants and interviews

PwsMS participating in an interview were mainly German (84.6%), had a mean EDSS of 6.8 (range: 6–8) and MS for 13.5 years (median: 14; SD: 8.1). For detailed characteristics see Table  3 .

Most of the interviewed caregivers (9 caregivers) were the partners of the PwsMS with whom they lived in the same household. For further details see Table  3 .

HCSs involved in the study comprised various professions, including MS-nurse (3), neurologist (2), general physician with further training in palliative care (1), physician with further training in palliative care and pain therapist (1), housing counselling service (1), outpatient nursing service manager (1), participation counselling service (1).

Structuring qualitative content analysis

The experiences of PwsMS, caregivers and HCSs were a priori deductively assigned to four main categories: (1) gatekeeper function, (2) broker function, (3) advocacy function [ 25 , 26 , 27 , 28 , 29 ] and (4) Outlook on CCM in standard care, whereas the subcategories were developed inductively (see Fig.  3 ).

Category system including main and subcategories of the qualitative thematic content analysis

The most extensive category, housing the highest number of codes and subcodes, was the “ Outlook on CCM in standard care ” (281 codes). Following this, the category “ Advocacy Function ” contained 261 codes. The “ Broker Function ” (150 codes) and the “ Gatekeeper Function ” (160 codes) constituted two smaller categories. The majority of codes was identified in the caregivers’ interviews, followed by those of PwsMS (see Table  4 ). Illustrative quotes for each category and subcategory can be found in Table  5 .

Persons with severe multiple sclerosis

In the gatekeeper function (59 codes), PwsMS particularly valued the CCM as a continuous contact person . They appreciated the CCM as a person of trust who was reliably accessible throughout the intervention period. This aspect, with 41 codes, held significant importance for PwsMS.

Within the broker function (44 codes), establishing contact was most important for PwsMS (22 codes). This involved the CCM as successfully connecting PwsMS and caregivers with physicians and therapists, as well as coordinating and arranging medical appointments, which were highly valued. Assistance in authority and health and social insurance matters (10 codes) was another subcategory, where the CCM encompassed support in communication with health insurance companies, such as improving the level of care, assisting with retirement pension applications, and facilitating rehabilitation program applications. Optimized care (12 codes) resulted in improved living conditions and the provision of assistive devices through the CCM intervention.

The advocacy function (103 codes) emerged as the most critical aspect for PwsMS, representing the core of the category system. PwsMS experienced multidimensional, comprehensive, cross-insurance system support from the CCM. This category, with 43 statements, was the largest within all subcategories. PwsMS described the CCM as addressing their concerns, providing help, and assisting with the challenges posed by the illness in everyday life. The second-largest subcategory, regaining, maintaining and supporting autonomy (25 codes), highlighted the CCM’s role in supporting self-sufficiency and independence. Reviving personal wellbeing (17 codes) involved PwsMSs’ needs of regaining positive feelings, improved quality of life, and a sense of support and acceptance, which could be improved by the CCM. Temporal relief (18 codes) was reported, with the CCM intervention taking over or reducing tasks.

Within the outlook on CCM in standard care (84 codes), eight subcategories were identified. Communications was described as friendly and open (9 codes), with the setting of communication (29 codes) including the frequency of contacts deemed appropriate by the interviewed PwsMS, who preferred face-to-face contact over virtual or telephone interactions. Improvement suggestions for CCM (10 codes) predominantly revolved around the desire for the continuation of the CCM beyond the trial, expressing intense satisfaction with the CCM contact person and program. PwsMS rarely wished for better cooperation with the CCM. With respect to limitations (7 codes), PwsMS distinguished between individual limitations (e.g. when not feeling ready for using a wheelchair) and overriding structural limitations (e.g. unsuccessful search for an accessible apartment despite CCM support). Some PwsMS mentioned needing the CCM earlier in the course of the disease and believed it would beneficial for anyone with a chronic illness (6 codes).

In the gatekeeper function (75 codes), caregivers highly valued the CCM as a continuous contact partner (33 codes). More frequently than among the PwsMS interviewed, caregivers valued the CCM as a source of consultation/ information on essential individual subjects (42 codes). The need for basic information about the illness, its potential course, treatment and therapy options, possible supportive equipment, and basic medical advice/ information could be met by the CCM.

Within the broker function (63 codes), caregivers primarily experienced the subcategory establish contacts (24 codes). They found the CCM as helpful in establishing and managing contact with physicians, therapists and especially with health insurance companies. In the subcategory assistance in authority and health and social insurance matters (22 codes), caregivers highlighted similar aspects as the PwsMS interviewed. However, there was a particular emphasis on assistance with patients' retirement matters. Caregivers also valued the optimization of patients’ care and living environment (17 codes) in various life areas during the CCM intervention, including improved access to assistive devices, home modification, and involvement of a household support and/ or nursing services.

The advocacy function, with 115 codes, was by far the broadest category . The subcategory multidimensional, comprehensive, cross-insurance system support represented the largest subcategory of caregivers, with 70 statements. In summary, caregivers felt supported by the CCM in all domains of life. Regaining, maintaining and supporting autonomy (11 codes) and reviving personal wellbeing (8 codes) in the form of an improved quality of life played a role not only for patients but also for caregivers, albeit to a lower extend. Caregivers experienced temporal relief (26 codes) as the CCM undertook a wide range of organizational tasks, freeing up more needed resources for their own interests.

For the Outlook on CCM in standard care , caregivers provided various suggestions (81 codes). Similar to PwsMS, caregivers felt that setting (home based face-to-face, telephone, virtual) and frequency of contact were appropriate (10 codes, communication setting ) and communications (7 codes) were recognized as open and friendly. However, to avoid conflicts between caregiver and PwsMS, caregivers preferred meeting the CCM separately from the PwsMS in the future. Some caregivers wished the CCM to specify all services it might offer at the beginning, while others emphasized not wanting this. Like PwsMS, caregivers criticized the CCM intervention being (trial-related) limited to one year, regardless of whether further support was needed or processes being incomplete (13 codes, improvement suggestions ). After the CCM intervention time had expired, the continuous contact person and assistance were missed and new problems had arisen and had to be managed with their own resources again (9 codes, effects of CCM discontinuation ), which was perceived as an exhausting or unsolvable endeavor. Caregivers identified analogous limitations (8 codes), both individual and structural. However, the largest subcategory, was the experienced potential of CCM (27 codes), reflected in extremely high satisfaction with the CCM intervention. Like PwsMS, caregivers regarded severe chronically ill persons in general as target groups for a CCM (7 codes) and would implement it even earlier, starting from the time of diagnosis. They considered a CCM to be particularly helpful for patients without caregivers or for caregivers with limited (time) resources, as it was true for most caregivers.

Health care specialists

In the gatekeeper function (26 codes) HCSs particularly valued the CCM as a continuous contact partner (18 codes). They primarily described their valuable collaboration with the CCM, emphasizing professional exchange between the CCM and HCSs.

Within the broker function (43 codes), the CCM was seen as a connecting link between patients and HCSs, frequently establishing contacts (18 codes). This not only improved optimal care on an individual patient level (case management) but also at a higher, superordinate care level (care management). HCSs appreciated the optimized care and living environment (18 codes) for PwsMS, including improved medical and therapeutic access and the introduction of new assistive devices. The CCM was also recognized as providing assistance in authority and health and social matters (7 codes) for PwsMS and their caregivers.

In the advocacy function (43 codes), HCSs primarily reported temporal relief through CCM intervention (23 codes). They experienced this relief, especially as the CCM provided multidimensional, comprehensive, and cross-insurance system support (15 codes) for PwsMS and their caregivers. Through this support, HCSs felt relieved from time intensive responsibilities that may not fall within their area of expertise, freeing up more time resources for their actual professional tasks.

The largest category within the HCSs interviews was the outlook on CCM in standard care (116 codes). In the largest subcategory, HCSs made suggestions for further patient groups who could benefit (38 codes) from a CCM. Chronic neurological diseases like neurodegenerative diseases (e.g. amyotrophic lateral sclerosis), typical and atypical Parkinson syndromes were mentioned. HCSs considered the enrollment of the CCM directly after the diagnosis of these complex chronic diseases. Additionally, chronic progressive diseases in general or oncological diseases, which may also run chronically, were regarded worthwhile for this approach. HCSs also provided suggestions regarding improvement (21 codes). They wished e.g. for information or contact when patients were enrolled to the CCM, regular updates, exchange and collaborative effort. On the other hand, HCSs reported, that their suggestions for improvement would hardly be feasible due to their limited time resources. Similar to patients and caregivers, HCSs experienced structural limits (13 codes), which a CCM could not exceed due to overriding structural limitations (e.g. insufficient supply of (household) aids, lack of outreach services like psychotherapists, and long processing times on health and pension insurers' side). HCSs were also asked about their opinions on financial resources (14 codes) of a CCM in standard care. All interviewed HCSs agreed that CCM would initially cause more costs for health and social insurers, but they were convinced of cost savings in the long run. HCSs particularly perceived the potential of the CCM (20 codes) through the feedback of PwsMS, highlighting the trustful relationship enabling individualized help for PwsMS and their caregivers.

Persons with severe multiple sclerosis and their caregivers

The long-term cross-sectoral CCM intervention implemented in the COCOS-MS trial addressed significant unmet needs of PwsMS and their caregivers which previous research revealed as burdensome and hardly or even not possible to improve without assistance [ 5 , 6 , 9 , 10 , 33 , 35 , 46 ]. Notably, the CCM service met the need for a reliable, continuous contact partner, guiding patients through the complexities of regulations, authorities and the insurance system. Both, PwsMS and their caregivers highly valued the professional, objective perspective provided by the CCM, recognizing it as a source of relief, support and improved care in line with previous studies [ 37 , 47 ]. Caregivers emphasized the CCM’s competence in offering concrete assistance and information on caregiving and the fundamentals of MS, including bureaucratic, authority and insurances matters. On the other hand, PwsMS particularly appreciated the CCMs external reflective and advisory function, along with empathic social support tailored to their individual concerns. Above all, the continuous partnership of trust, available irrespective of the care sector, was a key aspect that both PwsMS and their caregivers highlighted. This consistent support was identified as one of the main components in the care of PwsMS in previous studies [ 5 , 33 , 35 ].

As the health literacy is inadequate or problematic for 54% of the German population and disintegration in the health and social care system is high [ 30 , 31 , 32 ], the CCM approach serves to enhance health literacy and reduce disintegration of PwsMS and their caregivers by providing cross-insurance navigational guidance in the German health and social insurance sector on a superordinate level. Simultaneously PwsMS and caregivers experienced relief and gained more (time) resources for all areas of life outside of the disease and its management, including own interests and establishing biographical continuity. This empowerment enables patients to find a sense of purpose beyond their illness, regain autonomy, and enhance social participation, reducing the feeling of being a burden to those closest to them. Such feelings are often experienced as burdensome and shameful by PwsMS [ 6 , 48 , 49 , 50 ]. Finding a sense of purpose beyond the illness also contributes to caregivers perceiving their loved ones not primarily as patient but as individuals outside of the disease, reinforcing valuable relationships such as partners, siblings, or children, strengthening emotional bonds. These factors are also highly relevant and well-documented in a suicide-preventive context, as the suicide rate is higher in persons diagnosed with neurological disorders [ 19 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] and the feeling of being a burden to others, loss of autonomy, and perceived loss of dignity are significant factors in patients with severe chronic neurological diseases for suicide [ 50 , 57 ].

The temporal relief experienced by the CCM was particularly significant for HCSs and did not only improve the satisfaction of HCSs but also removed unfulfilled expectations and concerns about being blamed by patients when expectations could not be met, which previous studied elaborated [ 35 , 36 ]. Moreover, the CCM alleviated the burden on HCSs by addressing patients’ concerns, allowing them to focus on their own medical responsibilities. This aspect probably reduced the dissatisfaction that arises when HCSs are expected to address issues beyond their medical expertise, such as assistive devices, health and social insurance, and the organization and coordination of supplementary therapies, appointments, and contacts [ 35 , 36 , 61 ]. Consequently, the CCM reduced difficulties of HCSs treating persons with neurological or chronical illnesses, which previous research identified as problematic.

HCSs perceive their work as increasingly condensed with numerous time and economic constraints, especially when treating complex and severely ill individuals like PwsMS [ 36 ]. This constraint was mentioned by HCSs in the interviews and was one of the main reasons why they were hesitant to participate in interviews and may also be an explanation for a shorter interview duration than initially planned in the interview guides. The CCM’s overarching navigational competence in the health and social insurance system was particularly valued by HCSs. The complex and often small-scale specialties in the health and social care system are not easily manageable or well-known even for HCSs, and dealing with them can exceed their skills and time capacities [ 61 ]. The CCM played a crucial role in keeping (temporal) resources available for what HCSs are professionally trained and qualified to work on. However, there remains a challenge in finding solutions to the dilemma faced by HCSs regarding their wish to be informed about CCM procedures and linked with each other, while also managing the strain of additional requests and contact with the CCM due to limited (time) resources [ 62 ]. Hudon et al. (2023) suggest that optimizing time resources and improving exchange could involve meetings, information sharing via fax, e-mail, secure online platforms, or, prospectively, within the electronic patient record (EPR). The implementation of an EPR has shown promise in improving the quality of health care and time resources, when properly implemented [ 63 , 64 ]. The challenge lies ineffective information exchange between HCSs and CCM for optimal patient care. The prospect of time saving in the long run and at best for a financial incentive, e.g., when anchoring in the Social Security Code, will help best to win over the HCSs.If this crucial factor can be resolved, there is a chance that HCSs will thoroughly accept the CCM as an important pillar, benefiting not only PwsMS but also other complex patient groups, especially those with long-term neurological or complex oncological conditions that might run chronically.

Care and case management and implications for the health care system

The results of our study suggest that the cross-sectoral long-term advocacy CCM in the COCOS-MS trial, with continuous personal contacts at short intervals and constant reevaluation of needs, problems, resources and goals, is highly valued by PwsMS, caregivers, and HCSs. The trial addresses several key aspects that may have been overlooked in previous studies which have shown great potential for the integration of case management [ 17 , 47 , 62 , 65 , 66 ]. However, they often excluded the overriding care management, missed those patient groups with special severity and complexity who might struggle to reach social and health care structures independently or the interventions were not intended for long-term [ 22 , 37 ]. Our results indicate that the CCM intervention had a positive impact on PwsMS and caregivers as HCSs experienced them with benefits such as increased invigoration, reduced demands, and enhanced self-confidence. However, there was a notable loss experienced by PwsMS and caregivers after the completion of the CCM intervention, even if they had stabilized during the intervention period. The experiences of optimized social and health care for the addressed population, both at an individual and superordinate care level, support the integration of this service into standard care. Beyond the quantitatively measurable outcomes and economic considerations reported elsewhere [ 16 , 20 , 21 ], our results emphasize the importance of regaining control, self-efficacy, self-worth, dignity, autonomy, and social participation. These aspects are highlighted as preventive measures in suicidal contexts, which is particularly relevant for individuals with severe and complex illnesses [ 19 , 50 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. Our findings further emphasize the societal responsibilities to offer individuals with severe and complex illnesses the opportunity to regain control and meaningful aspects of life, irrespective of purely economic considerations. This underscores the need for a comprehensive evaluation that not only takes into account quantitative measures but also the qualitative aspects of well-being and quality of life when making recommendations of a CCM in standard care.

The study by J. Y. Joo and Huber (2019) highlighted that CM interventions aligned with the standards of the Case Management Society of America varied in duration, ranging from 1 month to 15.9 years, and implemented in community- or hospital-based settings. However, they noted a limitation in understanding how CM processes unfold [ 67 ]. In contrast, our trial addressed this criticism by providing transparent explanations of the CCM process, which also extends to a superordinate care management [ 40 , 41 ]. Our CCM manual [ 40 ] outlines a standardized and structured procedure for measuring and reevaluating individual resources, problems, and unmet needs on predefined dimensions. It also identifies goals and actions at reducing unmet needs and improving the individual resources of PwsMS and caregivers. Importantly, the CCM manual demonstrates that the CCM process can be structured and standardized, while accounting for the unique aspects of each individual’s serious illness, disease courses, complex needs, available resources, and environmental conditions. Furthermore, the adaptability of the CCM manual to other complex chronically ill patient groups suggests the potential for a standardized approach in various health care settings. This standardized procedure allows for consistency in assessing and addressing the individual needs of patients, ensuring that the CCM process remains flexible while maintaining a structured and goal-oriented framework.

The discussion about the disintegration in the social and health care system and the increasing specialization dates back to 2009 [ 31 , 32 ]. Three strategies were identified to address this issue: (a) “driver-minimizing” [Treiberminimierende], (b) “effect-modifying” [Effektmodifizierende] and (c) “disintegration-impact-minimizing” [Desintegrationsfolgenminimierende] strategies. “Driver-minimizing strategies” involve comprehensive and radical changes within the existing health and social care system, requiring political and social pursuit. “Disintegration-impact-minimizing strategies” are strategies like quality management or tele-monitoring, which are limited in scope and effectiveness. “Effect-modifying strategies”, to which CCM belongs, acknowledges the segmentation within the system but aims to overcome it through cooperative, communicative, and integrative measures. CCM, being an “effect-modifying strategy”, operates the “integrated segmentation model” [Integrierte Segmentierung] rather than the “general contractor model” [Generalunternehmer-Modell] or “total service provider model” [Gesamtdienstleister-Modell] [ 31 , 32 ]. In this model, the advantage lies in providing an overarching and coordinating service to link different HCSs and services cross-sectorally. The superordinate care management aspect of the CCM plays a crucial role in identifying gaps in care, which is essential for future development strategies within the health and social care system. It aims to find or develop (regional) alternatives to ensure optimal care [ 17 , 23 , 24 , 68 , 69 ], using regional services of existing health and social care structures. Therefore, superordinate care management within the CCM process is decisive for reducing disintegration in the system.

Strengths and limitations

The qualitative study results of the explorative COCOS-MS clinical trial, which employed an integrated mixed-method design, provide valuable insights into the individual experiences of three leading stakeholders: PwsMS, caregivers and HCSs with a long-term cross-sectoral CCM. In addition to in-depth interviews, patient and caregiver reported outcome measurements were utilized and will be reported elsewhere. The qualitative study’s strengths include the inclusion of patients who, due to the severity of their condition (e.g. EDSS mean: 6.8, range: 6–8, highly active MS), age (mean: 53.9 years, range: 36–73 years) family constellations, are often underrepresented in research studies and often get lost in existing social and health care structures. The study population is specific to the wider district region of Cologne, but the broad inclusion criteria make it representative of severe MS in Germany. The methodological approach of a deductive and inductive structuring content analysis made it possible to include new findings into an existing theoretical framework.

However, the study acknowledges some limitations. While efforts were made to include more HCSs, time constraints on their side limited the number of interviews conducted and might have biased the results. Some professions are underrepresented in the interviews. Complex symptoms (e.g. fatigue, ability to concentrate), medical or therapeutic appointments and organization of the everyday live may have been reasons for the patients’ and caregivers’ interviews lasting shorter than initially planned.

The provision of functions of a CCM, might have pre-structured the answers of the participants.

At current, there is no support system for PwsMS, their caregivers and HCSs that addresses their complex and unmet needs comprehensively and continuously. There are rare qualitative insights of the three important stakeholders: PwsMS, caregivers and HCSs in one analysis about a supporting service like a CCM. In response to this gap, we developed and implemented a long-term cross-sectoral advocacy CCM and analyzed it qualitatively. PwsMS, their caregivers and HCSs expressed positive experiences, perceiving the CCM as a source of relief and support that improved care across various aspects of life. For patients, the CCM intervention resulted in enhanced autonomy, reviving of personal wellbeing and new established contacts with HCSs. Caregivers reported a reduced organizational burden and felt better informed, and HCSs experienced primarily temporal relief, allowing them to concentrate on their core professional responsibilities. At a higher level of care, the study suggests that the CCM contributed to a reduction in disintegration within the social and health care system.

The feedback from participants is seen as valuable for adapting the CCM intervention and the CCM manual for follow-up studies, involving further complex patient groups such as neurological long-term diseases apart from MS and tailoring the duration of the intervention depending on the complexity of evolving demands.

Availability of data and materials

Generated and/or analyzed datasets of participants are available from the corresponding author on reasonable request to protect participants. Preliminary partial results have been presented as a poster during the EAPC World Congress in June 2023 and the abstract has been published in the corresponding abstract booklet [ 70 ].

Abbreviations

Amyotrophic lateral sclerosis

• Care and case management

Case management

Central nervous system

Communication, Coordination and security for people with multiple sclerosis

Consolidated criteria for reporting qualitative research

German register for clinical studies

Extended disability status scale

Electronic patient record

Quality of life

Multiple sclerosis

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Acknowledgements

We would like to thank all the patients, caregivers and health care specialists who volunteered their time to participate in an interview and the trial, Carola Janßen for transcribing the interviews, Fiona Brown for translating the illustrative quotes and Beatrix Münzberg, Kerstin Weiß and Monika Höveler for data collection in the quantitative study part.

COCOS-MS Trial Group

Anne Müller 1 , Fabian Hebben 1 , Kim Dillen 1 , Veronika Dunkl 1 , Yasemin Goereci 2 , Raymond Voltz 1,3,4 , Peter Löcherbach 5 , Clemens Warnke 2 , Heidrun Golla 1 , Dirk Müller 6 , Dorthe Hobus 1 , Eckhard Bonmann 7 , Franziska Schwartzkopff 8 , Gereon Nelles 9 , Gundula Palmbach 8 , Herbert Temmes 10 , Isabel Franke 1 , Judith Haas 10 , Julia Strupp 1 , Kathrin Gerbershagen 7 , Laura Becker-Peters 8 , Lothar Burghaus 11 , Martin Hellmich 12 , Martin Paus 8 , Solveig Ungeheuer 1 , Sophia Kochs 1 , Stephanie Stock 6 , Thomas Joist 13 , Volker Limmroth 14

1 Department of Palliative Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

2 Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

3 Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), University of Cologne, Cologne, Germany

4 Center for Health Services Research (ZVFK), University of Cologne, Cologne, Germany

5 German Society of Care and Case Management e.V. (DGCC), Münster, Germany

6 Institute for Health Economics and Clinical Epidemiology (IGKE), Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

7 Department of Neurology, Klinikum Köln, Cologne, Germany

8 Clinical Trials Centre Cologne (CTCC), Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

9 NeuroMed Campus, MedCampus Hohenlind, Cologne, Germany

10 German Multiple Sclerosis Society Federal Association (DMSG), Hannover, Germany

11 Department of Neurology, Heilig Geist-Krankenhaus Köln, Cologne, Germany

12 Institute of Medical Statistics and Computational Biology (IMSB), Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

13 Academic Teaching Practice, University of Cologne, Cologne, Germany

14 Department of Neurology, Klinikum Köln-Merheim, Cologne, Germany

Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Innovation Funds of the Federal Joint Committee (G-BA), grant number: 01VSF19029.

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Anne Müller, Fabian Hebben, Kim Dillen, Veronika Dunkl, Raymond Voltz & Heidrun Golla

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• Anne Müller
• , Fabian Hebben
• , Kim Dillen
• , Veronika Dunkl
• , Yasemin Goereci
• , Raymond Voltz
• , Peter Löcherbach
• , Clemens Warnke
• , Heidrun Golla
• , Dirk Müller
• , Dorthe Hobus
• , Eckhard Bonmann
• , Franziska Schwartzkopff
• , Gereon Nelles
• , Gundula Palmbach
• , Herbert Temmes
• , Isabel Franke
• , Judith Haas
• , Julia Strupp
• , Kathrin Gerbershagen
• , Laura Becker-Peters
• , Lothar Burghaus
• , Martin Hellmich
• , Martin Paus
• , Solveig Ungeheuer
• , Sophia Kochs
• , Stephanie Stock
• , Thomas Joist
•  & Volker Limmroth

Contributions

HG, KD, CW designed the trial. HG, KD obtained ethical approvals. HG, KD developed the interview guidelines with help of the CCM (SU). AM was responsible for collecting qualitative data, developing the code system, coding, analysis of the data and writing the first draft of the manuscript, thoroughly revised and partly rewritten by HG. FH supported in collecting qualitative data, coding and analysis of the interviews. KD supported in collecting qualitative data. AM, FH, KD, VD, YG, RV, PL, CW, HG discussed and con-solidated the finalized category system. AM, FH, KD, VD, YG, RV, PL, CW, HG read and commented on the manuscript and agreed to the final version.

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Participants were provided with oral and written information about the trial and provided written informed consent. Ethical approval was obtained from the Ethics Committee of the University of Cologne (#20–1436). The trial is registered in the German Register for Clinical Studies (DRKS) (DRKS00022771) and is conducted under the Declaration of Helsinki.

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Clemens Warnke has received institutional support from Novartis, Alexion, Sanofi Genzyme, Janssen, Biogen, Merck and Roche. The other authors declare that they have no competing interests.

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Müller, A., Hebben, F., Dillen, K. et al. “So at least now I know how to deal with things myself, what I can do if it gets really bad again”—experiences with a long-term cross-sectoral advocacy care and case management for severe multiple sclerosis: a qualitative study. BMC Health Serv Res 24 , 453 (2024). https://doi.org/10.1186/s12913-024-10851-1

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What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

• Trisha Greenhalgh   ORCID: orcid.org/0000-0003-2369-8088 1 ,
• Julie L. Darbyshire 1 ,
• Cassie Lee 2 ,
• Emma Ladds 1 &
• Jenny Ceolta-Smith 3  

BMC Medicine volume  22 , Article number:  159 ( 2024 ) Cite this article

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Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called “postcode lottery” of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in services—evolved to focus on examining the reasons why standardizing care was so challenging in this condition.

In 2021–2023, we ran a quality improvement collaborative across 10 UK sites. The dataset reported here was mostly but not entirely qualitative. It included data on the origins and current context of each clinic, interviews with staff and patients, and ethnographic observations at 13 clinics (50 consultations) and 45 multidisciplinary team (MDT) meetings (244 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles) and philosophy of knowledge.

Participating clinics made progress towards standardizing assessment and management in some topics; some variation remained but this could usually be explained. Clinics had different histories and path dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including a high proportion of patients with comorbidities. A key mechanism for achieving high-quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic (case-based) reasoning , in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients).

Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied and comorbidities common, generic “evidence-based” standards require much individual adaptation. In this complex condition, quality improvement resources may be productively spent supporting MDTs to optimise their case-based learning through interdisciplinary discussion. Quality assessment of a long covid service should include review of a sample of individual cases to assess how guidelines have been interpreted and personalized to meet patients’ unique needs.

Study registration

NCT05057260, ISRCTN15022307.

Peer Review reports

The term “long covid” [ 1 ] means prolonged symptoms following SARS-CoV-2 infection not explained by an alternative diagnosis [ 2 ]. It embraces the US term “post-covid conditions” (symptoms beyond 4 weeks) [ 3 ], the UK terms “ongoing symptomatic covid-19” (symptoms lasting 4–12 weeks) and “post covid-19 syndrome” (symptoms beyond 12 weeks) [ 4 ] and the World Health Organization’s “post covid-19 condition” (symptoms occurring beyond 3 months and persisting for at least 2 months) [ 5 ]. Long covid thus defined is extremely common. In UK, for example, 1.8 million of a population of 67 million met the criteria for long covid in early 2023 and 41% of these had been unwell for more than 2 years [ 6 ].

Long covid is characterized by a constellation of symptoms which may include breathlessness, fatigue, muscle and joint pain, chest pain, memory loss and impaired concentration (“brain fog”), sleep disturbance, depression, anxiety, palpitations, dizziness, gastrointestinal problems such as diarrhea, skin rashes and allergy to food or drugs [ 2 ]. These lead to difficulties with essential daily activities such as washing and dressing, impaired exercise tolerance and ability to work, and reduced quality of life [ 2 , 7 , 8 ]. Symptoms typically cluster (e.g. in different patients, long covid may be dominated by fatigue, by breathlessness or by palpitations and dizziness) [ 9 , 10 ]. Long covid may follow a fairly constant course or a relapsing and remitting one, perhaps with specific triggers [ 11 ]. Overlaps between fatigue-dominant subtypes of long covid, myalgic encephalomyelitis and chronic fatigue syndrome have been hypothesized [ 12 ] but at the time of writing remain unproven.

Long covid has been a contested condition from the outset. Whilst long-term sequelae following other coronavirus (SARS and MERS) infections were already well-documented [ 13 ], SARS-CoV-2 was originally thought to cause a short-lived respiratory illness from which the patient either died or recovered [ 14 ]. Some clinicians dismissed protracted or relapsing symptoms as due to anxiety or deconditioning, especially if the patient had not had laboratory-confirmed covid-19. People with long covid got together in online groups and shared accounts of their symptoms and experiences of such “gaslighting” in their healthcare encounters [ 15 , 16 ]. Some groups conducted surveys on their members, documenting the wide range of symptoms listed in the previous paragraph and showing that whilst long covid is more commonly a sequel to severe acute covid-19, it can (rarely) follow a mild or even asymptomatic acute infection [ 17 ].

Early publications on long covid depicted a post-pneumonia syndrome which primarily affected patients who had been hospitalized (and sometimes ventilated) [ 18 , 19 ]. Later, covid-19 was recognized to be a multi-organ inflammatory condition (the pneumonia, for example, was reclassified as pneumonitis ) and its long-term sequelae attributed to a combination of viral persistence, dysregulated immune response (including auto-immunity), endothelial dysfunction and immuno-thrombosis, leading to damage to the lining of small blood vessels and (thence) interference with transfer of oxygen and nutrients to vital organs [ 20 , 21 , 22 , 23 , 24 ]. But most such studies were highly specialized, laboratory-based and written primarily for an audience of fellow laboratory researchers. Despite demonstrating mean differences in a number of metabolic variables, they failed to identify a reliable biomarker that could be used routinely in the clinic to rule a diagnosis of long covid in or out. Whilst the evidence base from laboratory studies grew rapidly, it had little influence on clinical management—partly because most long covid clinics had been set up with impressive speed by front-line clinical teams to address an immediate crisis, with little or no input from immunologists, virologists or metabolic specialists [ 25 ].

Studies of the patient experience revealed wide geographical variation in whether any long covid services were provided and (if they were) which patients were eligible for these and what tests and treatments were available [ 26 ]. An interim UK clinical guideline for long covid had been produced at speed and published in December 2020 [ 27 ], but it was uncertain about diagnostic criteria, investigations, treatments and prognosis. Early policy recommendations for long covid services in England, based on wide consultation across UK, had proposed a tiered service with “tier 1” being supported self-management, “tier 2” generalist assessment and management in primary care, “tier 3” specialist rehabilitation or respiratory follow-up with oversight from a consultant physician and “tier 4” tertiary care for patients with complications or complex needs [ 28 ]. In 2021, ring-fenced funding was allocated to establish 90 multidisciplinary long covid clinics in England [ 29 ]; some clinics were also set up with local funding in Scotland and Wales. These clinics varied widely in eligibility criteria, referral pathways, staffing mix (some had no doctors at all) and investigations and treatments offered. A further policy document on improving long covid services was published in 2022 [ 30 ]; it recommended that specialist long covid clinics should continue, though the long-term funding of these services remains uncertain [ 31 ]. To build the evidence base for delivering long covid services, major programs of publicly funded research were commenced in both UK [ 32 ] and USA [ 33 ].

In short, at the time this study began (late 2021), there appeared to be much scope for a program of quality improvement which would capture fast-emerging research findings, establish evidence-based standards and ensure these were rapidly disseminated and consistently adopted across both specialist long covid services and in primary care.

Quality improvement collaboratives

The quality improvement movement in healthcare was born in the early 1980s when clinicians and policymakers US and UK [ 34 , 35 , 36 , 37 ] began to draw on insights from outside the sector [ 38 , 39 , 40 ]. Adapting a total quality management approach that had previously transformed the Japanese car industry, they sought to improve efficiency, reduce waste, shift to treating the upstream causes of problems (hence preventing disease) and help all services approach the standards of excellence achieved by the best. They developed an approach based on (a) understanding healthcare as a complex system (especially its key interdependencies and workflows), (b) analysing and addressing variation within the system, (c) learning continuously from real-world data and (d) developing leaders who could motivate people and help them change structures and processes [ 41 , 42 , 43 , 44 ].

Quality improvement collaboratives (originally termed “breakthrough collaboratives” [ 45 ]), in which representatives from different healthcare organizations come together to address a common problem, identify best practice, set goals, share data and initiate and evaluate improvement efforts [ 46 ], are one model used to deliver system-wide quality improvement. It is widely assumed that these collaboratives work because—and to the extent that—they identify, interpret and implement high-quality evidence (e.g. from randomized controlled trials).

Research on why quality improvement collaboratives succeed or fail has produced the following list of critical success factors: taking a whole-system approach, selecting a topic and goal that fits with organizations’ priorities, fostering a culture of quality improvement (e.g. that quality is everyone’s job), engagement of everyone (including the multidisciplinary clinical team, managers, patients and families) in the improvement effort, clearly defining people’s roles and contribution, engaging people in preliminary groundwork, providing organizational-level support (e.g. chief executive endorsement, protected staff time, training and support for teams, resources, quality-focused human resource practices, external facilitation if needed), training in specific quality improvement techniques (e.g. plan-do-study-act cycle), attending to the human dimension (including cultivating trust and working to ensure shared vision and buy-in), continuously generating reliable data on both processes (e.g. current practice) and outcomes (clinical, satisfaction) and a “learning system” infrastructure in which knowledge that is generated feeds into individual, team and organizational learning [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].

The quality improvement collaborative approach has delivered many successes but it has been criticized at a theoretical level for over-simplifying the social science of human motivation and behaviour and for adopting a somewhat mechanical approach to the study of complex systems [ 55 , 56 ]. Adaptations of the original quality improvement methodology (e.g. from Sweden [ 57 , 58 ]) have placed greater emphasis on human values and meaning-making, on the grounds that reducing the complexities of a system-wide quality improvement effort to a set of abstract and generic “success factors” will miss unique aspects of the case such as historical path dependencies, personalities, framing and meaning-making and micropolitics [ 59 ].

Perhaps this explains why, when the abovementioned factors are met, a quality improvement collaborative’s success is more likely but is not guaranteed, as a systematic review demonstrated [ 60 ]. Some well-designed and well-resourced collaboratives addressing clear knowledge gaps produced few or no sustained changes in key outcome measures [ 49 , 53 , 60 , 61 , 62 ]. To identify why this might be, a detailed understanding of a service’s history, current challenges and contextual constraints is needed. This explains our decision, part-way through the study reported here, to collect rich contextual data on participating sites so as to better explain success or failure of our own collaborative.

Warranted and unwarranted variation in clinical practice

A generation ago, Wennberg described most variation in clinical practice as “unwarranted” (which he defined as variation in the utilization of health care services that cannot be explained by variation in patient illness or patient preferences) [ 63 ]. Others coined the term “postcode lottery” to depict how such variation allegedly impacted on health outcomes [ 64 ]. Wennberg and colleagues’ Atlas of Variation , introduced in 1999 [ 65 ], and its UK equivalent, introduced in 2010 [ 66 ], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt services to identify and address examples of under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [ 66 , 67 , 68 , 69 ]. Sutherland and Levesque’s proposed framework for analysing variation, for example, has three domains: capacity (broadly, whether sufficient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed), and agency (e.g. whether clinicians are engaged with the issue and the effect of patient choice) [ 70 ].

Whilst it is clearly a good idea to identify unwarranted variation in practice, it is also important to acknowledge that variation can be warranted . The very act of measuring and describing variation carries great rhetorical power, since revealing geographical variation in any chosen metric effectively frames this as a problem with a conceptually simple solution (reducing variation) that will appeal to both politicians and the public [ 71 ]. The temptation to expose variation (e.g. via visualizations such as maps) and address it in mechanistic ways should be resisted until we have fully understood the reasons why it exists, which may include perverse incentives, insufficient opportunities to discuss cases with colleagues, weak or absent feedback on practice, unclear decision processes, contested definitions of appropriate care and professional challenges to guidelines [ 72 ].

Research question, aims and objectives

Research question.

What is quality in long covid care and how can it best be achieved?

To identify best practice and reduce unwarranted variation in UK long covid services.

To explain aspects of variation in long covid services that are or may be warranted.

Our original objectives were to:

Establish a quality improvement collaborative for 10 long covid clinics across UK.

Use quality improvement methods in collaboration with patients and clinic staff to prioritize aspects of care to improve. For each priority topic, identify best (evidence-informed) clinical practice, measure performance in each clinic, compare performance with a best practice benchmark and improve performance.

Produce organizational case studies of participating long covid clinics to explain their origins, evolution, leadership, ethos, population served, patient pathways and place in the wider healthcare ecosystem.

Examine these case studies to explain variation in practice, especially in topics where the quality improvement cycle proves difficult to follow or has limited impact.

The LOCOMOTION study

LOCOMOTION (LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS) was a 30-month multi-site case study of 10 long covid clinics (8 in England, 1 in Wales and 1 in Scotland), beginning in 2021, which sought to optimise long covid care. Each clinic offered multidisciplinary care to patients referred from primary or secondary care (and, in some cases, self-referred), and held regular multidisciplinary team (MDT) meetings, mostly online via Microsoft Teams, to discuss cases. A study protocol for LOCOMOTION, with details of ethical approvals, management, governance and patient involvement has been published [ 25 ]. The three main work packages addressed quality improvement, technology-supported patient self-management and phenotyping and symptom clustering. This paper reports on the first work package, focusing mainly on qualitative findings.

Setting up the quality improvement collaborative

We broadly followed standard methodology for “breakthrough” quality improvement collaboratives [ 44 , 45 ], with two exceptions. First, because of geographical distance, continuing pandemic precautions and developments in videoconferencing technology, meetings were held online. Second, unlike in the original breakthrough model, patients were included in the collaborative, reflecting the cultural change towards patient partnerships since the model was originally proposed 40 years ago.

Each site appointed a clinical research fellow (doctor, nurse or allied health professional) funded partly by the LOCOMOTION study and partly with clinical sessions; some were existing staff who were backfilled to take on a research role whilst others were new appointments. The quality improvement meetings were held approximately every 8 weeks on Microsoft Teams and lasted about 2 h; there was an agenda and a chair, and meetings were recorded with consent. The clinical research fellow from each clinic attended, sometimes joined by the clinical lead for that site. In the initial meeting, the group proposed and prioritized topics before merging their consensus with the list of priority topics generated separately by patients (there was much overlap but also some differences).

In subsequent meetings, participants attempted to reach consensus on how to define, measure and achieve quality for each priority topic in turn, implement this approach in their own clinic and monitor its impact. Clinical leads prepared illustrative clinical cases and summaries of the research evidence, which they presented using Microsoft Powerpoint; the group then worked towards consensus on the implications for practice through general discussion. Clinical research fellows assisted with literature searches, collected baseline data from their own clinic, prepared and presented anonymized case examples, and contributed to collaborative goal-setting for improvement. Progress on each topic was reviewed at a later meeting after an agreed interval.

An additional element of this work package was semi-structured interviews with 29 patients, recruited from 9 of the 10 participating sites, about their clinic experiences with a view to feeding into service improvement (in the other site, no patient volunteered).

Our patient advisory group initially met separately from the quality improvement collaborative. They designed a short survey of current practice and sent it to each clinic; the results of this informed a prioritization exercise for topics where they considered change was needed. The patient-generated list was tabled at the quality improvement collaborative discussions, but patients were understandably keen to join these discussions directly. After about 9 months, some patient advisory group members joined the regular collaborative meetings. This dynamic was not without its tensions, since sharing performance data requires trust and there were some concerns about confidentiality when real patient cases were discussed with other patients present.

How evidence-informed quality targets were set

At the time the study began, there were no published large-scale randomized controlled trials of any interventions for long covid. We therefore followed a model used successfully in other quality improvement efforts where research evidence was limited or absent or it did not translate unambiguously into models for current services. In such circumstances, the best evidence may be custom and practice in the best-performing units. The quality improvement effort becomes oriented to what one group of researchers called “potentially better practices”—that is, practices that are “developed through analysis of the processes of care, literature review, and site visits” (page 14) [ 73 ]. The idea was that facilitated discussion among clinical teams, drawing on published research where available but also incorporating clinical experience, established practice and systematic analysis of performance data across participating clinics would surface these “potentially better practices”—an approach which, though not formally tested in controlled trials, appears to be associated with improved outcomes [ 46 , 73 ].

Adding an ethnographic component

Following limited progress made on some topics that had been designated high priority, we interviewed all 10 clinical research fellows (either individually or, in two cases, with a senior clinician present) and 18 other clinic staff (five individually plus two groups of 5 and 8), along with additional informal discussions, to explore the challenges of implementing the changes that had been agreed. These interviews were not audiotaped but detailed notes were made and typed up immediately afterwards. It became evident that some aspects of what the collaborative had deemed “evidence-informed” care were contested by front-line clinic staff, perceived as irrelevant to the service they were delivering, or considered impossible to implement. To unpack these issues further, the research protocol was amended to include an ethnographic component.

TG and EL (academic general practitioners) and JLD (a qualitative researcher with a PhD in the patient experience) attended a total of 45 MDT meetings in participating clinics (mostly online or hybrid). Staff were informed in advance that there would be an observer present; nobody objected. We noted brief demographic and clinical details of cases discussed (but no identifying data), dilemmas and uncertainties on which discussions focused, and how different staff members contributed.

TG made 13 in-person visits to participating long covid clinics. Staff were notified in advance; all were happy to be observed. Visits lasted between 5 and 8 h (54 h in total). We observed support staff booking patients in and processing requests and referrals, and shadowed different clinical staff in turn as they saw patients. Patients were informed of our presence and its purpose beforehand and given the opportunity to decline (three of 53 patients approached did). We discussed aspects of each case with the clinician after the patient left. When invited, we took breaks with staff and used these as an opportunity to ask them informally what it was like working in the clinic.

Ethnographic observation, analysis and reporting was geared to generating a rich interpretive account of the clinical, operational and interpersonal features of each clinic—what Van Maanen calls an “impressionist tales” [ 74 ]. Our work was also guided by the principles set out by Golden-Biddle and Locke, namely authenticity (spending time in the field and basing interpretations on these direct observations), plausibility (creating a plausible account through rich persuasive description) and criticality (e.g. reflexively examining our own assumptions) [ 75 ]. Our collection and analysis of qualitative data was informed by our own professional backgrounds (two general practitioners, one physical therapist, two non-clinicians).

In both MDTs and clinics, we took contemporaneous notes by hand and typed these up immediately afterwards.

Data management and analysis

Typed interview notes and field notes from clinics were collated in a set of Word documents, one for each clinic attended. They were analysed thematically [ 76 ] with attention to the literature on quality improvement and variation (see “ Background ”). Interim summaries were prepared on each clinic, setting out the narrative of how it had been established, its ethos and leadership, setting and staffing, population served and key links with other parts of the local healthcare ecosystem.

Minutes and field notes from the quality improvement collaborative meetings were summarized topic by topic, including initial data collected by the researchers-in-residence, improvement actions taken (or attempted) in that clinic, and any follow-up data shared. Progress or lack of it was interpreted in relation to the contextual case summary for that clinic.

Patient cases seen in clinic, and those discussed by MDTs, were summarized as brief case narratives in Word documents. Using the constant comparative method [ 77 ], we produced an initial synthesis of the clinical picture and principles of management based on the first 10 patient cases seen, and refined this as each additional case was added. Demographic and brief clinical and social details were also logged on Excel spreadsheets. When writing up clinical cases, we used the technique of composite case construction (in which we drew on several actual cases to generate a fictitious one, thereby protecting anonymity whilst preserving key empirical findings [ 78 ]); any names reported in this paper are pseudonyms.

Member checking

A summary was prepared for each clinic, including a narrative of the clinic’s own history and a summary of key quality issues raised across the ten clinics. These summaries included examples from real cases in our dataset. These were shared with the clinical research fellow and a senior clinician from the clinic, and amended in response to feedback. We also shared these summaries with representatives from the patient advisory group.

Overview of dataset

This study generated three complementary datasets. First, the video recordings, minutes, and field notes of 12 quality improvement collaborative meetings, along with the evidence summaries prepared for these meetings and clinic summaries (e.g. descriptions of current practice, audits) submitted by the clinical research fellows. This dataset illustrated wide variation in practice, and (in many topics) gaps or ambiguities in the evidence base.

Second, interviews with staff ( n  = 30) and patients ( n  = 29) from the clinics, along with ethnographic field notes (approximately 100 pages) from 13 in-person clinic visits (54 h), including notes on 50 patient consultations (40 face-to-face, 6 telephone, 4 video). This dataset illustrated the heterogeneity among the ten participating clinics.

Third, field notes (approximately 100 pages), including discussions on 244 clinical cases from the 45 MDT meetings (49 h) that we observed. This dataset revealed further similarities and contrasts among clinics in how patients were managed. In particular, it illustrated how, for the complex patients whose cases were presented at these meetings, teams made sense of, and planned for, each case through multidisciplinary dialogue. This dialogue typically began with one staff member presenting a detailed clinical history along with a narrative of how it had affected the patient’s life and what was at stake for them (e.g. job loss), after which professionals from various backgrounds (nursing, physical therapy, occupational therapy, psychology, dietetics, and different medical specialties) joined in a discussion about what to do.

The ten participating sites are summarized in Table  1 .

In the next two sections, we explore two issues—difficulty defining best practice and the heterogeneous nature of the clinics—that were key to explaining why quality, when pursued in a 10-site collaborative, proved elusive. We then briefly summarize patients’ accounts of their experience in the clinics and give three illustrative examples of the elusiveness of quality improvement using selected topics that were prioritized in our collaborative: outcome measures, investigation of palpitations and management of fatigue. In the final section of the results, we describe how MDT deliberations proved crucial for local quality improvement. Further detail on clinical priority topics will be presented in a separate paper.

“Best practice” in long covid: uncertainty and conflict

The study period (September 2021 to December 2023) corresponded with an exponential increase in published research on long covid. Despite this, the quality improvement collaborative found few unambiguous recommendations for practice. This gap between what the research literature offered and what clinical practice needed was partly ontological (relating what long covid is ). One major bone of contention between patients and clinicians (also evident in discussions with our patient advisory group), for example, was how far (and in whom) clinicians should look for and attempt to treat the various metabolic abnormalities that had been documented in laboratory research studies. The literature on this topic was extensive but conflicting [ 20 , 21 , 22 , 23 , 24 , 79 , 80 , 81 , 82 ]; it was heavy on biological detail but light on clinical application.

Patients were often aware of particular studies that appeared to offer plausible molecular or cellular explanations for symptom clusters along with a drug (often repurposed and off-label) whose mechanism of action appeared to be a good fit with the metabolic chain of causation. In one clinic, for example, we were shown an email exchange between a patient (not medically qualified) and a consultant, in which the patient asked them to reconsider their decision not to prescribe low-dose naltrexone, an opioid receptor antagonist with anti-inflammatory properties. The request included a copy of a peer-reviewed academic paper describing a small, uncontrolled pre-post study (i.e. a weak study design) in which this drug appeared to improve symptoms and functional performance in patients with long covid, as well as a mechanistic argument explaining why the patient felt this drug was a plausible choice in their own case.

This patient’s clinician, in common with most clinicians delivering front-line long covid services, considered that the evidence for such mechanism-based therapies was weak. Clinicians generally felt that this evidence, whilst promising, did not yet support routine measurement of clotting factors, antibodies, immune cells or other biomarkers or the prescription of mechanism-based therapies such as antivirals, anti-inflammatories or anticoagulants. Low-dose naltroxone, for example, is currently being tested in at least one randomized controlled trial (see National Clinical Trials Registry NCT05430152), which had not reported at the time of our observations.

Another challenge to defining best practice was the oft-repeated phrase that long covid is a “diagnosis by exclusion”, but the high prevalence of comorbidities meant that the “pure” long covid patient untainted by other potential explanations for their symptoms was a textbook ideal. In one MDT, for example, we observed a discussion about a patient who had had both swab-positive covid-19 and erythema migrans (a sign of Lyme disease) in the weeks before developing fatigue, yet local diagnostic criteria for each condition required the other to be excluded.

The logic of management in most participating clinics was pragmatic: prompt multidisciplinary assessment and treatment with an emphasis on obtaining a detailed clinical history (including premorbid health status), excluding serious complications (“red flags”), managing specific symptom clusters (for example, physical therapy for breathing pattern disorder), treating comorbidities (for example, anaemia, diabetes or menopause) and supporting whole-person rehabilitation [ 7 , 83 ]. The evidentiary questions raised in MDT discussions (which did not include patients) addressed the practicalities of the rehabilitation model (for example, whether cognitive therapy for neurocognitive complications is as effective when delivered online as it is when delivered in-person) rather than the molecular or cellular mechanisms of disease. For example, the question of whether patients with neurocognitive impairment should be tested for micro-clots or treated with anticoagulants never came up in the MDTs we observed, though we did visit a tertiary referral clinic (the tier 4 clinic in site H), whose lead clinician had a research interest in inflammatory coagulopathies and offered such tests to selected patients.

Because long covid typically produces dozens of symptoms that tend to be uniquely patterned in each patient, the uncertainties on which MDT discussions turned were rarely about general evidence of the kind that might be found in a guideline (e.g. how should fatigue be managed?). Rather they concerned particular case-based clinical decisions (e.g. how should this patient’s fatigue be managed, given the specifics of this case?). An example from our field notes illustrates this:

Physical therapist presents the case of a 39-year-old woman who works as a cleaner on an overnight ferry. Has had long covid for 2 years. Main symptoms are shortness of breath and possible anxiety attacks, especially when at work. She has had a course of physical therapy to teach diaphragmatic breathing but has found that focusing on her breathing makes her more anxious. Patient has to do a lot of bending in her job (e.g. cleaning toilets and under seats), which makes her dizzy, but Active Stand Test was normal. She also has very mild tricuspid incompetence [someone reads out a cardiology report—not hemodynamically significant].

Rehabilitation guidelines (e.g. WHO) recommend phased return to work (e.g. with reduced hours) and frequent breaks. “Tricky!” says someone. The job is intense and busy, and the patient can’t afford not to work. Discussion on whether all her symptoms can be attributed to tension and anxiety. Physical therapist who runs the breathing group says, “No, it’s long covid”, and describes severe initial covid-19 episode and results of serial chest X-rays which showed gradual clearing of ground glass shadows. Team discussion centers on how to negotiate reduced working hours in this particular job, given the overnight ferry shifts. --MDT discussion, Site D

This example raises important considerations about the nature of clinical knowledge in long covid. We return to it in the final section of the “ Results ” and in the “ Discussion ”.

Long covid clinics: a heterogeneous context for quality improvement

Most participating clinics had been established in mid-2020 to follow up patients who had been hospitalized (and perhaps ventilated) for severe acute covid-19. As mass vaccination reduced the severity of acute covid-19 for most people, the patient population in all clinics progressively shifted to include fewer “post-ICU [intensive care unit]” patients (in whom respiratory symptoms almost always dominated), and more people referred by their general practitioners or other secondary care specialties who had not been hospitalized for their acute covid-19 infection, and in whom fatigue, brain fog and palpitations were often the most troubling symptoms. Despite these similarities, the ten clinics had very different histories, geographical and material settings, staffing structures, patient pathways and case mix, as Table  1 illustrates. Below, we give more detail on three example sites.

Site C was established as a generalist “assessment-only” service by a general practitioner with an interest in infectious diseases. It is led jointly by that general practitioner and an occupational therapist, assisted by a wide range of other professionals including speech and language therapy, dietetics, clinical psychology and community-based physical therapy and occupational therapy. It has close links with a chronic fatigue service and a pain clinic that have been running in the locality for over 20 years. The clinic, which is entirely virtual (staff consult either from home or from a small side office in the community trust building), is physically located in a low-rise building on the industrial outskirts of a large town, sharing office space with various community-based health and social care services. Following a 1-h telephone consultation by one of the clinical leads, each patient is discussed at the MDT and then either discharged back to their general practitioner with a detailed management plan or referred on to one of the specialist services. This arrangement evolved to address a particular problem in this locality—that many patients with long covid were being referred by their general practitioner to multiple specialties (e.g. respiratory, neurology, fatigue), leading to a fragmented patient experience, unnecessary specialist assessments and wasteful duplication. The generalist assessment by telephone is oriented to documenting what is often a complex illness narrative (including pre-existing physical and mental comorbidities) and working with the patient to prioritize which symptoms or problems to pursue in which order.

Site E, in a well-regarded inner-city teaching hospital, had been set up in 2020 by a respiratory physician. Its initial ethos and rationale had been “respiratory follow-up”, with strong emphasis on monitoring lung damage via repeated imaging and lung function tests and in ensuring that patients received specialist physical therapy to “re-learn” efficient breathing techniques. Over time, this site has tried to accommodate a more multi-system assessment, with the introduction of a consultant-led infectious disease clinic for patients without a dominant respiratory component, reflecting the shift towards a more fatigue-predominant case mix. At the time of our fieldwork, each patient was seen in turn by a physician, psychologist, occupational therapist and respiratory physical therapist (half an hour each) before all four staff reconvened in a face-to-face MDT meeting to form a plan for each patient. But whilst a wide range of patients with diverse symptoms were discussed at these meetings, there remained a strong focus on respiratory pathology (e.g. tracking improvements in lung function and ensuring that coexisting asthma was optimally controlled).

Site F, one of the first long covid clinics in UK, was set up by a rehabilitation consultant who had been drafted to work on the ICU during the first wave of covid-19 in early 2020. He had a longstanding research interest in whole-patient rehabilitation, especially the assessment and management of chronic fatigue and pain. From the outset, clinic F was more oriented to rehabilitation, including vocational rehabilitation to help patients return to work. There was less emphasis on monitoring lung function or pursuing respiratory comorbidities. At the time of our fieldwork, clinic F offered both a community-based service (“tier 2”) led by an occupational therapist, supported by a respiratory physical therapist and psychologist, and a hospital-based service (“tier 3”) led by the rehabilitation consultant, supported by a wider MDT. Staff in both tiers emphasized that each patient needs a full physical and mental assessment and help to set and work towards achievable goals, whilst staying within safe limits so as to avoid post-exertional symptom exacerbation. Because of the research interest of the lead physician, clinic F adapted well to the growing numbers of patients with fatigue and quickly set up research studies on this cohort [ 84 ].

Details of the other seven sites are shown in Table  1 . Broadly speaking, sites B, E, G and H aligned with the “respiratory follow-up” model and sites F and I aligned with the “rehabilitation” model. Sites A and J had a high-volume, multi-tiered service whose community tier aligned with the “holistic GP assessment” model (site C above) and which also offered a hospital-based, rehabilitation-focused tier. The small service in Scotland (site D) had evolved from an initial respiratory focus to become part of the infectious diseases (ME/CFS) service; Lyme disease (another infectious disease whose sequelae include chronic fatigue) was also prevalent in this region.

The patient experience

Whilst the 10 participating clinics were very diverse in staffing, ethos and patient flows, the 29 patient interviews described remarkably consistent clinic experiences. Almost all identified the biggest problem to be the extended wait of several months before they were seen and the limited awareness (when initially referred) of what long covid clinics could provide. Some talked of how they cried with relief when they finally received an appointment. When the quality improvement collaborative was initially established, waiting times and bottlenecks were patients’ the top priority for quality improvement, and this ranking was shared by clinic staff, who were very aware of how much delays and uncertainties in assessment and treatment compounded patients’ suffering. This issue resolved to a large extent over the study period in all clinics as the referral backlog cleared and the incidence of new cases of long covid fell [ 85 ]; it will be covered in more detail in a separate publication.

Most patients in our sample were satisfied with the care they received when they were finally seen in clinic, especially how they finally felt “heard” after a clinician took a full history. They were relieved to receive affirmation of their experience, a diagnosis of what was wrong and reassurance that they were believed. They were grateful for the input of different members of the multidisciplinary teams and commented on the attentiveness, compassion and skill of allied professionals in particular (“she was wonderful, she got me breathing again”—patient BIR145 talking about a physical therapist). One or two patient participants expressed confusion about who exactly they had seen and what advice they had been given, and some did not realize that a telephone assessment had been an actual clinical consultation. A minority expressed disappointment that an expected investigation had not been ordered (one commented that they had not had any blood tests at all). Several had assumed that the help and advice from the long covid clinic would continue to be offered until they were better and were disappointed that they had been discharged after completing the various courses on offer (since their clinic had been set up as an “assessment only” service).

In the next sections, we give examples of topics raised in the quality improvement collaborative and how they were addressed.

Example quality topic 1: Outcome measures

The first topic considered by the quality improvement collaborative was how (that is, using which measures and metrics) to assess and monitor patients with long covid. In the absence of a validated biomarker, various symptom scores and quality of life scales—both generic and disease-specific—were mooted. Site F had already developed and validated a patient-reported outcome measure (PROM), the C19-YRS (Covid-19 Yorkshire Rehabilitation Scale) and used it for both research and clinical purposes [ 86 ]. It was quickly agreed that, for the purposes of generating comparative research findings across the ten clinics, the C19-YRS should be used at all sites and completed by patients three-monthly. A commercial partner produced an electronic version of this instrument and an app for patient smartphones. The quality improvement collaborative also agreed that patients should be asked to complete the EUROQOL EQ5D, a widely used generic health-related quality of life scale [ 87 ], in order to facilitate comparisons between long covid and other chronic conditions.

In retrospect, the discussions which led to the unopposed adoption of these two measures as a “quality” initiative in clinical care were somewhat aspirational. A review of progress at a subsequent quality improvement meeting revealed considerable variation among clinics, with a wide variety of measures used in different clinics to different degrees. Reasons for this variation were multiple. First, although our patient advisory group were keen that we should gather as much data as possible on the patient experience of this new condition, many clinic patients found the long questionnaires exhausting to complete due to cognitive impairment and fatigue. In addition, whilst patients were keen to answer questions on symptoms that troubled them, many had limited patience to fill out repeated surveys on symptoms that did not trouble them (“it almost felt as if I’ve not got long covid because I didn’t feel like I fit the criteria as they were laying it out”—patient SAL001). Staff assisted patients in completing the measures when needed, but this was time-consuming (up to 45 min per instrument) and burdensome for both staff and patients. In clinics where a high proportion of patients required assistance, staff time was the rate-limiting factor for how many instruments got completed. For some patients, one short instrument was the most that could be asked of them, and the clinician made a judgement on which one would be in their best interests on the day.

The second reason for variation was that the clinical diagnosis and management of particular features, complications and comorbidities of long covid required more nuance than was provided by these relatively generic instruments, and the level of detail sought varied with the specialist interest of the clinic (and the clinician). The modified C19-YRS [ 88 ], for example, contained 19 items, of which one asked about sleep quality. But if a patient had sleep difficulties, many clinicians felt that these needed to be documented in more detail—for example using the 8-item Epworth Sleepiness Scale, originally developed for conditions such as narcolepsy and obstructive sleep apnea [ 89 ]. The “Epworth score” was essential currency for referrals to some but not all specialist sleep services. Similarly, the C19-YRS had three items relating to anxiety, depression and post-traumatic stress disorder, but in clinics where there was a strong focus on mental health (e.g. when there was a resident psychologist), patients were usually invited to complete more specific tools (e.g. the Patient Health Questionnaire 9 [ 90 ], a 9-item questionnaire originally designed to assess severity of depression).

The third reason for variation was custom and practice. Ethnographic visits revealed that paper copies of certain instruments were routinely stacked on clinicians’ desks in outpatient departments and also (in some cases) handed out by administrative staff in waiting areas so that patients could complete them before seeing the clinician. These familiar clinic artefacts tended to be short (one-page) instruments that had a long tradition of use in clinical practice. They were not always fit for purpose. For example, the Nijmegen questionnaire was developed in the 1980s to assess hyperventilation; it was validated against a longer, “gold standard” instrument for that condition [ 91 ]. It subsequently became popular in respiratory clinics to diagnose or exclude breathing pattern disorder (a condition in which the normal physiological pattern of breathing becomes replaced with less efficient, shallower breathing [ 92 ]), so much so that the researchers who developed the instrument published a paper to warn fellow researchers that it had not been validated for this purpose [ 93 ]. Whilst a validated 17-item instrument for breathing pattern disorder (the Self-Evaluation of Breathing Questionnaire [ 94 ]) does exist, it is not in widespread clinical use. Most clinics in LOCOMOTION used Nijmegen either on all patients (e.g. as part of a comprehensive initial assessment, especially if the service had begun as a respiratory follow-up clinic) or when breathing pattern disorder was suspected.

In sum, the use of outcome measures in long covid clinics was a compromise between standardization and contingency. On the one hand, all clinics accepted the need to use “validated” instruments consistently. On the other hand, there were sometimes good reasons why they deviated from agreed practice, including mismatch between the clinic’s priorities as a research site, its priorities as a clinical service, and the particular clinical needs of a patient; the clinic’s—and the clinician’s—specialist focus; and long-held traditions of using particular instruments with which staff and patients were familiar.

Example quality topic 2: Postural orthostatic tachycardia syndrome (POTS)

Palpitations (common in long covid) and postural orthostatic tachycardia syndrome (POTS, a disproportionate acceleration in heart rate on standing, the assumed cause of palpitations in many long covid patients) was the top priority for quality improvement identified by our patient advisory group. Reflecting discussions and evidence (of various kinds) shared in online patient communities, the group were confident that POTS is common in long covid patients and that many cases remain undetected (perhaps misdiagnosed as anxiety). Their request that all long covid patients should be “screened” for POTS prompted a search for, and synthesis of, evidence (which we published in the BMJ [ 95 ]). In sum, that evidence was sparse and contested, but, combined with standard practice in specialist clinics, broadly supported the judicious use of the NASA Lean Test [ 96 ]. This test involves repeated measurements of pulse and blood pressure with the patient first lying and then standing (with shoulders resting against a wall).

The patient advisory group’s request that the NASA Lean Test should be conducted on all patients met with mixed responses from the clinics. In site F, the lead physician had an interest in autonomic dysfunction in chronic fatigue and was keen; he had already published a paper on how to adapt the NASA Lean Test for self-assessment at home [ 97 ]. Several other sites were initially opposed. Staff at site E, for example, offered various arguments:

The test is time-consuming, labor-intensive, and takes up space in the clinic which has an opportunity cost in terms of other potential uses;

The test is unvalidated and potentially misleading (there is a high incidence of both false negative and false positive results);

There is no proven treatment for POTS, so there is no point in testing for it;

It is a specialist test for a specialist condition, so it should be done in a specialist clinic where its benefits and limitations are better understood;

Objective testing does not change clinical management since what we treat is the patient’s symptoms (e.g. by a pragmatic trial of lifestyle measures and medication);

People with symptoms suggestive of dysautonomia have already been “triaged out” of this clinic (that is, identified in the initial telephone consultation and referred directly to neurology or cardiology);

POTS is a manifestation of the systemic nature of long covid; it does not need specific treatment but will improve spontaneously as the patient goes through standard interventions such as active pacing, respiratory physical therapy and sleep hygiene;

Testing everyone, even when asymptomatic, runs counter to the ethos of rehabilitation, which is to “de-medicalize” patients so as to better orient them to their recovery journey.

When clinics were invited to implement the NASA Lean Test on a consecutive sample of patients to resolve a dispute about the incidence of POTS (from “we’ve only seen a handful of people with it since the clinic began” to “POTS is common and often missed”), all but one site agreed to participate. The tertiary POTS centre linked to site H was already running the NASA Lean Test as standard on all patients. Site C, which operated entirely virtually, passed the work to the referring general practitioner by making this test a precondition for seeing the patient; site D, which was largely virtual, sent instructions for patients to self-administer the test at home.

The NASA Lean Test study has been published separately [ 98 ]. In sum, of 277 consecutive patients tested across the eight clinics, 20 (7%) had a positive NASA Lean Test for POTS and a further 28 (10%) a borderline result. Six of 20 patients who met the criteria for POTS on testing had no prior history of orthostatic intolerance. The question of whether this test should be used to “screen” all patients was not answered definitively. But the experience of participating in the study persuaded some sceptics that postural changes in heart rate could be severe in some long covid patients, did not appear to be fully explained by their previously held theories (e.g. “functional”, anxiety, deconditioning), and had likely been missed in some patients. The outcome of this particular quality improvement cycle was thus not a wholescale change in practice (for which the evidence base was weak) but a more subtle increase in clinical awareness, a greater willingness to consider testing for POTS and a greater commitment to contribute to research into this contested condition.

More generally, the POTS audit prompted some clinicians to recognize the value of quality improvement in novel clinical areas. One physician who had initially commented that POTS was not seen in their clinic, for example, reflected:

“ Our clinic population is changing. […] Overall there’s far fewer post-ICU patients with ECMO [extra-corporeal membrane oxygenation] issues and far more long covid from the community, and this is the bit our clinic isn’t doing so well on. We’re doing great on breathing pattern disorder; neuro[logists] are helping us with the brain fogs; our fatigue and occupational advice is ok but some of the dysautonomia symptoms that are more prevalent in the people who were not hospitalized – that’s where we need to improve .” -Respiratory physician, site G (from field visit 6.6.23)

Example quality topic 3: Management of fatigue

Fatigue was the commonest symptom overall and a high priority among both patients and clinicians for quality improvement. It often coexisted with the cluster of neurocognitive symptoms known as brain fog, with both conditions relapsing and remitting in step. Clinicians were keen to systematize fatigue management using a familiar clinical framework oriented around documenting a full clinical history, identifying associated symptoms, excluding or exploring comorbidities and alternative explanations (e.g. poor sleep patterns, depression, menopause, deconditioning), assessing how fatigue affects physical and mental function, implementing a program of physical and cognitive therapy that was sensitive to the patient’s condition and confidence level, and monitoring progress using validated patient-reported outcome measures and symptom diaries.

The underpinning logic of this approach, which broadly reflected World Health Organization guidance [ 99 ], was that fatigue and linked cognitive impairment could be a manifestation of many—perhaps interacting—conditions but that a whole-patient (body and mind) rehabilitation program was the cornerstone of management in most cases. Discussion in the quality improvement collaborative focused on issues such as whether fatigue was so severe that it produced safety concerns (e.g. in a person’s job or with childcare), the pros and cons of particular online courses such as yoga, relaxation and mindfulness (many were viewed positively, though the evidence base was considered weak), and the extent to which respiratory physical therapy had a crossover impact on fatigue (systematic reviews suggested that it may do, but these reviews also cautioned that primary studies were sparse, methodologically flawed, and heterogeneous [ 100 , 101 ]). They also debated the strengths and limitations of different fatigue-specific outcome measures, each of which had been developed and validated in a different condition, with varying emphasis on cognitive fatigue, physical fatigue, effect on daily life, and motivation. These instruments included the Modified Fatigue Impact Scale; Fatigue Severity Scale [ 102 ]; Fatigue Assessment Scale; Functional Assessment Chronic Illness Therapy—Fatigue (FACIT-F) [ 103 ]; Work and Social Adjustment Scale [ 104 ]; Chalder Fatigue Scale [ 105 ]; Visual Analogue Scale—Fatigue [ 106 ]; and the EQ5D [ 87 ]. In one clinic (site F), three of these scales were used in combination for reasons discussed below.

Some clinicians advocated melatonin or nutritional supplements (such as vitamin D or folic acid) for fatigue on the grounds that many patients found them helpful and formal placebo-controlled trials were unlikely ever to be conducted. But neurostimulants used in other fatigue-predominant conditions (e.g. brain injury, stroke), which also lacked clinical trial evidence in long covid, were viewed as inappropriate in most patients because of lack of evidence of clear benefit and hypothetical risk of harm (e.g. adverse drug reactions, polypharmacy).

Whilst the patient advisory group were broadly supportive of a whole-patient rehabilitative approach to fatigue, their primary concern was fatiguability , especially post-exertional symptom exacerbation (PESE, also known as “crashes”). In these, the patient becomes profoundly fatigued some hours or days after physical or mental exertion, and this state can last for days or even weeks [ 107 ]. Patients viewed PESE as a “red flag” symptom which they felt clinicians often missed and sometimes caused. They wanted the quality improvement effort to focus on ensuring that all clinicians were aware of the risks of PESE and acted accordingly. A discussion among patients and clinicians at a quality improvement collaborative meeting raised a new research hypothesis—that reducing the number of repeated episodes of PESE may improve the natural history of long covid.

These tensions around fatigue management played out differently in different clinics. In site C (the GP-led virtual clinic run from a community hub), fatigue was viewed as one manifestation of a whole-patient condition. The lead general practitioner used the metaphor of untangling a skein of wool: “you have to find the end and then gently pull it”. The underlying problem in a fatigued patient, for example, might be an undiagnosed physical condition such as anaemia, disturbed sleep, or inadequate pacing. These required (respectively) the chronic fatigue service (comprising an occupational therapist and specialist psychologist and oriented mainly to teaching the techniques of goal-setting and pacing), a “tiredness” work-up (e.g. to exclude anaemia or menopause), investigation of poor sleep (which, not uncommonly, was due to obstructive sleep apnea), and exploration of mental health issues.

In site G (a hospital clinic which had evolved from a respiratory service), patients with fatigue went through a fatigue management program led by the occupational therapist with emphasis on pacing, energy conservation, avoidance of PESE and sleep hygiene. Those without ongoing respiratory symptoms were often discharged back to their general practitioner once they had completed this; there was no consultant follow-up of unresolved fatigue.

In site F (a rehabilitation clinic which had a longstanding interest in chronic fatigue even before the pandemic), active interdisciplinary management of fatigue was commenced at or near the patient’s first visit, on the grounds that the earlier this began, the more successful it would be. In this clinic, patients were offered a more intensive package: a similar occupational therapy-led fatigue course as those in site G, plus input from a dietician to advise on regular balanced meals and caffeine avoidance and a group-based facilitated peer support program which centred on fatigue management. The dietician spoke enthusiastically about how improving diet in longstanding long covid patients often improved fatigue (e.g. because they had often lost muscle mass and tended to snack on convenience food rather than make meals from scratch), though she agreed there was no evidence base from trials to support this approach.

Pursuing local quality improvement through MDTs

Whilst some long covid patients had “textbook” symptoms and clinical findings, many cases were unique and some were fiendishly complex. One clinician commented that, somewhat paradoxically, “easy cases” were often the post-ICU follow-ups who had resolving chest complications; they tended to do well with a course of respiratory physical therapy and a return-to-work program. Such cases were rarely brought to MDT meetings. “Difficult cases” were patients who had not been hospitalized for their acute illness but presented with a months- or years-long history of multiple symptoms with fatigue typically predominant. Each one was different, as the following example (some details of which have been fictionalized to protect anonymity) illustrates.

The MDT is discussing Mrs Fermah, a 65-year-old homemaker who had covid-19 a year ago. She has had multiple symptoms since, including fluctuating fatigue, brain fog, breathlessness, retrosternal chest pain of burning character, dry cough, croaky voice, intermittent rashes (sometimes on eating), lips going blue, ankle swelling, orthopnoea, dizziness with the room spinning which can be triggered by stress, low back pain, aches and pains in the arms and legs and pins and needles in the fingertips, loss of taste and smell, palpitations and dizziness (unclear if postural, but clear association with nausea), headaches on waking, and dry mouth. She is somewhat overweight (body mass index 29) and admits to low mood. Functionally, she is mostly confined to the house and can no longer manage the stairs so has begun to sleep downstairs. She has stumbled once or twice but not fallen. Her social life has ceased and she rarely has the energy to see her grandchildren. Her 70-year-old husband is retired and generally supportive, though he spends most evenings at his club. Comorbidities include glaucoma which is well controlled and overseen by an ophthalmologist, mild club foot (congenital) and stage 1 breast cancer 20 years ago. Various tests, including a chest X-ray, resting and exercise oximetry and a blood panel, were normal except for borderline vitamin D level. Her breathing questionnaire score suggests she does not have breathing pattern disorder. ECG showed first-degree atrioventricular block and left axis deviation. No clinician has witnessed the blue lips. Her current treatment is online group respiratory physical therapy; a home visit is being arranged to assess her climbing stairs. She has declined a psychologist assessment. The consultant asks the nurse who assessed her: “Did you get a feel if this is a POTS-type dizziness or an ENT-type?” She sighs. “Honestly it was hard to tell, bless her.”—Site A MDT

This patient’s debilitating symptoms and functional impairments could all be due to long covid, yet “evidence-based” guidance for how to manage her complex suffering does not exist and likely never will exist. The question of which (if any) additional blood or imaging tests to do, in what order of priority, and what interventions to offer the patient will not be definitively answered by consulting clinical trials involving hundreds of patients, since (even if these existed) the decision involves weighing this patient’s history and the multiple factors and uncertainties that are relevant in her case. The knowledge that will help the MDT provide quality care to Mrs Fermah is case-based knowledge—accumulated clinical experience and wisdom from managing and deliberating on multiple similar cases. We consider case-based knowledge further in the “ Discussion ”.

Summary of key findings

This study has shown that a quality improvement collaborative of UK long covid clinics made some progress towards standardizing assessment and management in some topics, but some variation remained. This could be explained in part by the fact that different clinics had different histories and path dependencies, occupied a different place in the local healthcare ecosystem, served different populations, were differently staffed, and had different clinical interests. Our patient advisory group and clinicians in the quality improvement collaborative broadly prioritized the same topics for improvement but interpreted them somewhat differently. “Quality” long covid care had multiple dimensions, relating to (among other things) service set-up and accessibility, clinical provision appropriate to the patient’s need (including options for referral to other services locally), the human qualities of clinical and support staff, how knowledge was distributed across (and accessible within) the system, and the accumulated collective wisdom of local MDTs in dealing with complex cases (including multiple kinds of specialist expertise as well as relational knowledge of what was at stake for the patient). Whilst both staff and patients were keen to contribute to the quality improvement effort, the burden of measurement was evident: multiple outcome measures, used repeatedly, were resource-intensive for staff and exhausting for patients.

Strengths and limitations of this study

To our knowledge, we are the first to report both a quality improvement collaborative and an in-depth qualitative study of clinical work in long covid. Key strengths of this work include the diverse sampling frame (with sites from three UK jurisdictions and serving widely differing geographies and demographics); the use of documents, interviews and reflexive interpretive ethnography to produce meaningful accounts of how clinics emerged and how they were currently organized; the use of philosophical concepts to analyse data on how MDTs produced quality care on a patient-by-patient basis; and the close involvement of patient co-researchers and coauthors during the research and writing up.

Limitations of the study include its exclusive UK focus (the external validity of findings to other healthcare systems is unknown); the self-selecting nature of participants in a quality improvement collaborative (our patient advisory group suggested that the MDTs observed in this study may have represented the higher end of a quality spectrum, hence would be more likely than other MDTs to adhere to guidelines); and the particular perspective brought by the researchers (two GPs, a physical therapist and one non-clinical person) in ethnographic observations. Hospital specialists or organizational scholars, for example, may have noticed different things or framed what they observed differently.

Explaining variation in long covid care

Sutherland and Levesque’s framework mentioned in the “ Background ” section does not explain much of the variation found in our study [ 70 ]. In terms of capacity, at the time of this study most participating clinics benefited from ring-fenced resources. In terms of evidence, guidelines existed and were not greatly contested, but as illustrated by the case of Mrs Fermah above, many patients were exceptions to the guideline because of complex symptomatology and relevant comorbidities. In terms of agency, clinicians in most clinics were passionately engaged with long covid (they were pioneers who had set up their local clinic and successfully bid for national ring-fenced resources) and were generally keen to support patient choice (though not if the patient requested tests which were unavailable or deemed not indicated).

Astma et al.’s list of factors that may explain variation in practice (see “ Background ”) includes several that may be relevant to long covid, especially that the definition of appropriate care in this condition remains somewhat contested. But lack of opportunity to discuss cases was not a problem in the clinics in our sample. On the contrary, MDT meetings in each locality gave clinicians multiple opportunities to discuss cases with colleagues and reflect collectively on whether and how to apply particular guidelines.

The key problem was not that clinicians disputed the guidelines for managing long covid or were unaware of them; it was that the guidelines were not self-interpreting . Rather, MDTs had to deliberate on the balance of benefits and harms in different aspects of individual cases. In patients whose symptoms suggested a possible diagnosis of POTS (or who suspected themselves of having POTS), for example, these deliberations were sometimes lengthy and nuanced. Should a test result that is not technically in the abnormal range but close to it be treated as diagnostic, given that symptoms point to this diagnosis? If not, should the patient be told that the test excludes POTS or that it is equivocal? If a cardiology opinion has stated firmly that the patient does not have POTS but the cardiologist is not known for their interest in this condition, should a second specialist opinion be sought? If the gold standard “tilt test” [ 108 ] for POTS (usually available only in tertiary centres) is not available locally, does this patient merit a costly out-of-locality referral? Should the patient’s request for a trial of off-label medication, reflecting discussions in an online support group, be honoured? These are the kinds of questions on which MDTs deliberated at length.

The fact that many cases required extensive deliberation does not necessarily justify variation in practice among clinics. But taking into account the clinics’ very different histories, set-up, and local referral pathways, the variation begins to make sense. A patient who is being assessed in a clinic that functions as a specialist chronic fatigue centre and attracts referrals which reflect this interest (e.g. site F in our sample) will receive different management advice from one that functions as a telephone-only generalist assessment centre and refers on to other specialties (site C in our sample). The wide variation in case mix, coupled with the fact that a different proportion of these cases were highly complex in each clinic (and in different ways), suggests that variation in practice may reflect appropriate rather than inappropriate care.

Our patient advisory group affirmed that many of the findings reported here resonated with their own experience, but they raised several concerns. These included questions about patient groups who may have been missed in our sample because they were rarely discussed in MDTs. The decision to take a case to MDT discussion is taken largely by a clinician, and there was evidence from online support groups that some patients’ requests for their case to be taken to an MDT had been declined (though not, to our knowledge, in the clinics participating in the LOCOMOTION study).

We began this study by asking “what is quality in long covid care?”. We initially assumed that this question referred to a generalizable evidence base, which we felt we could identify, and we believed that we could then determine whether long covid clinics were following the evidence base through conventional audits of structure, process, and outcome. In retrospect, these assumptions were somewhat naïve. On the basis of our findings, we suggest that a better (and more individualized) research question might be “to what extent does each patient with long covid receive evidence-based care appropriate to their needs?”. This question would require individual case review on a sample of cases, tracking each patient longitudinally including cross-referrals, and also interviewing the patient.

Nomothetic versus idiographic knowledge

In a series of lectures first delivered in the 1950s and recently republished [ 109 ], psychiatrist Dr Maurice O’Connor Drury drew on the later philosophy of his friend and mentor Ludwig Wittgenstein to challenge what he felt was a concerning trend: that the nomothetic (generalizable, abstract) knowledge from randomized controlled trials (RCTs) was coming to over-ride the idiographic (personal, situated) knowledge about particular patients. Based on Wittgenstein’s writings on the importance of the particular, Drury predicted—presciently—that if implemented uncritically, RCTs would result in worse, not better, care for patients, since it would go hand-in-hand with a downgrading of experience, intuition, subjective judgement, personal reflection, and collective deliberation.

Much conventional quality improvement methodology is built on an assumption that nomothetic knowledge (for example, findings from RCTs and systematic reviews) is a higher form of knowing than idiographic knowledge. But idiographic, case-based reasoning—despite its position at the very bottom of evidence-based medicine’s hierarchy of evidence [ 110 ]—is a legitimate and important element of medical practice. Bioethicist Kathryn Montgomery, drawing on Aristotle’s notion of praxis , considers clinical practice to be an example of case-based reasoning [ 111 ]. Medicine is governed not by hard and fast laws but by competing maxims or rules of thumb ; the essence of judgement is deciding which (if any) rule should be applied in a particular circumstance. Clinical judgement incorporates science (especially the results of well-conducted research) and makes use of available tools and technologies (including guidelines and decision-support algorithms that incorporate research findings). But rather than being determined solely by these elements, clinical judgement is guided both by the scientific evidence and by the practical and ethical question “what is it best to do, for this individual, given these circumstances?”.

In this study, we observed clinical management of, and MDT deliberations on, hundreds of clinical cases. In the more straightforward ones (for example, recovering pneumonitis), guideline-driven care was not difficult to implement and such cases were rarely brought to the MDT. But cases like Mrs Fermah (see last section of “ Results ”) required much discussion on which aspects of which guideline were in the patient’s best interests to bring into play at any particular stage in their illness journey.

Conclusions

One systematic review on quality improvement collaboratives concluded that “ [those] reporting success generally addressed relatively straightforward aspects of care, had a strong evidence base and noted a clear evidence-practice gap in an accepted clinical pathway or guideline” (page 226) [ 60 ]. The findings from this study suggest that to the extent that such collaboratives address clinical cases that are not straightforward, conventional quality improvement methods may be less useful and even counterproductive.

The question “what is quality in long covid care?” is partly a philosophical one. Our findings support an approach that recognizes and values idiographic knowledge —including establishing and protecting a safe and supportive space for deliberation on individual cases to occur and to value and draw upon the collective learning that occurs in these spaces. It is through such deliberation that evidence-based guidelines can be appropriately interpreted and applied to the unique needs and circumstances of individual patients. We suggest that Drury’s warning about the limitations of nomothetic knowledge should prompt a reassessment of policies that rely too heavily on such knowledge, resulting in one-size-fits-all protocols. We also cautiously hypothesize that the need to centre the quality improvement effort on idiographic rather than nomothetic knowledge is unlikely to be unique to long covid. Indeed, such an approach may be particularly important in any condition that is complex, unpredictable, variable in presentation and clinical course, and associated with comorbidities.

Availability of data and materials

Selected qualitative data (ensuring no identifiable information) will be made available to formal research teams on reasonable request to Professor Greenhalgh at the University of Oxford, on condition that they have research ethics approval and relevant expertise. The quantitative data on NASA Lean Test have been published in full in a separate paper [ 98 ].

Abbreviations

Chronic fatigue syndrome

Intensive care unit

Jenny Ceolta-Smith

Julie Darbyshire

LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS

Multidisciplinary team

Myalgic encephalomyelitis

Middle East Respiratory Syndrome

National Aeronautics and Space Association

Occupational therapy/ist

Post-exertional symptom exacerbation

Postural orthostatic tachycardia syndrome

Speech and language therapy

Severe Acute Respiratory Syndrome

Trisha Greenhalgh

United Kingdom

United States

World Health Organization

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Acknowledgements

We are grateful to clinic staff for allowing us to study their work and to patients for allowing us to sit in on their consultations. We also thank the funder of LOCOMOTION (National Institute for Health Research) and the patient advisory group for lived experience input.

This research is supported by National Institute for Health Research (NIHR) Long Covid Research Scheme grant (Ref COV-LT-0016).

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Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Rd, Oxford, OX2 6GG, UK

Trisha Greenhalgh, Julie L. Darbyshire & Emma Ladds

Imperial College Healthcare NHS Trust, London, UK

LOCOMOTION Patient Advisory Group and Lived Experience Representative, London, UK

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Contributions

TG conceptualized the overall study, led the empirical work, supported the quality improvement meetings, conducted the ethnographic visits, led the data analysis, developed the theorization and wrote the first draft of the paper. JLD organized and led the quality improvement meetings, supported site-based researchers to collect and analyse data on their clinic, collated and summarized data on quality topics, and liaised with the patient advisory group. CL conceptualized and led the quality topic on POTS, including exploring reasons for some clinics’ reluctance to conduct testing and collating and analysing the NASA Lean Test data across all sites. EL assisted with ethnographic visits, data analysis, and theorization. JCS contributed lived experience of long covid and also clinical experience as an occupational therapist; she liaised with the wider patient advisory group, whose independent (patient-led) audit of long covid clinics informed the quality improvement prioritization exercise. All authors provided extensive feedback on drafts and contributed to discussions and refinements. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Trisha Greenhalgh .

Ethics declarations

Ethics approval and consent to participate.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276) and subsequent amendments.

Patient participants in clinic were approached by the clinician (without the researcher present) and gave verbal informed consent for a clinically qualified researcher to observe the consultation. If they consented, the researcher was then invited to sit in. A written record was made in field notes of this verbal consent. It was impractical to seek consent from patients whose cases were discussed (usually with very brief clinical details) in online MDTs. Therefore, clinical case examples from MDTs presented in the paper are fictionalized cases constructed from multiple real cases and with key clinical details changed (for example, comorbidities were replaced with different conditions which would produce similar symptoms). All fictionalized cases were checked by our patient advisory group to check that they were plausible to lived experience experts.

Consent for publication

No direct patient cases are reported in this manuscript. For details of how the fictionalized cases were constructed and validated, see “Consent to participate” above.

Competing interests

TG was a member of the UK National Long Covid Task Force 2021–2023 and on the Oversight Group for the NICE Guideline on Long Covid 2021–2022. She is a member of Independent SAGE.

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Greenhalgh, T., Darbyshire, J.L., Lee, C. et al. What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography. BMC Med 22 , 159 (2024). https://doi.org/10.1186/s12916-024-03371-6

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Received : 04 December 2023

Accepted : 26 March 2024

Published : 15 April 2024

DOI : https://doi.org/10.1186/s12916-024-03371-6

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• Post-covid-19 syndrome
• Quality improvement
• Breakthrough collaboratives
• Warranted variation
• Unwarranted variation
• Improvement science
• Ethnography
• Idiographic reasoning
• Nomothetic reasoning

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