case study research qualitative content analysis

What Is Qualitative Content Analysis?

Qca explained simply (with examples).

By: Jenna Crosley (PhD). Reviewed by: Dr Eunice Rautenbach (DTech) | February 2021

If you’re in the process of preparing for your dissertation, thesis or research project, you’ve probably encountered the term “ qualitative content analysis ” – it’s quite a mouthful. If you’ve landed on this post, you’re probably a bit confused about it. Well, the good news is that you’ve come to the right place…

Overview: Qualitative Content Analysis

• What (exactly) is qualitative content analysis
• The two main types of content analysis
• When to use content analysis
• How to conduct content analysis (the process)
• The advantages and disadvantages of content analysis

1. What is content analysis?

Content analysis is a  qualitative analysis method  that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants – this is called  unobtrusive  research.

In other words, with content analysis, you don’t necessarily need to interact with participants (although you can if necessary); you can simply analyse the data that they have already produced. With this type of analysis, you can analyse data such as text messages, books, Facebook posts, videos, and audio (just to mention a few).

The basics – explicit and implicit content

When working with content analysis, explicit and implicit content will play a role. Explicit data is transparent and easy to identify, while implicit data is that which requires some form of interpretation and is often of a subjective nature. Sounds a bit fluffy? Here’s an example:

Joe: Hi there, what can I help you with? 

Lauren: I recently adopted a puppy and I’m worried that I’m not feeding him the right food. Could you please advise me on what I should be feeding? 

Joe: Sure, just follow me and I’ll show you. Do you have any other pets?

Lauren: Only one, and it tweets a lot!

In this exchange, the explicit data indicates that Joe is helping Lauren to find the right puppy food. Lauren asks Joe whether she has any pets aside from her puppy. This data is explicit because it requires no interpretation.

On the other hand, implicit data , in this case, includes the fact that the speakers are in a pet store. This information is not clearly stated but can be inferred from the conversation, where Joe is helping Lauren to choose pet food. An additional piece of implicit data is that Lauren likely has some type of bird as a pet. This can be inferred from the way that Lauren states that her pet “tweets”.

As you can see, explicit and implicit data both play a role in human interaction  and are an important part of your analysis. However, it’s important to differentiate between these two types of data when you’re undertaking content analysis. Interpreting implicit data can be rather subjective as conclusions are based on the researcher’s interpretation. This can introduce an element of bias , which risks skewing your results.

2. The two types of content analysis

Now that you understand the difference between implicit and explicit data, let’s move on to the two general types of content analysis : conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.

Conceptual analysis focuses on the number of times a concept occurs in a set of data and is generally focused on explicit data. For example, if you were to have the following conversation:

Marie: She told me that she has three cats.

Jean: What are her cats’ names?

Marie: I think the first one is Bella, the second one is Mia, and… I can’t remember the third cat’s name.

In this data, you can see that the word “cat” has been used three times. Through conceptual content analysis, you can deduce that cats are the central topic of the conversation. You can also perform a frequency analysis , where you assess the term’s frequency in the data. For example, in the exchange above, the word “cat” makes up 9% of the data. In other words, conceptual analysis brings a little bit of quantitative analysis into your qualitative analysis.

As you can see, the above data is without interpretation and focuses on explicit data . Relational content analysis, on the other hand, takes a more holistic view by focusing more on implicit data in terms of context, surrounding words and relationships.

There are three types of relational analysis:

• Affect extraction
• Proximity analysis
• Cognitive mapping

Affect extraction is when you assess concepts according to emotional attributes. These emotions are typically mapped on scales, such as a Likert scale or a rating scale ranging from 1 to 5, where 1 is “very sad” and 5 is “very happy”.

If participants are talking about their achievements, they are likely to be given a score of 4 or 5, depending on how good they feel about it. If a participant is describing a traumatic event, they are likely to have a much lower score, either 1 or 2.

Proximity analysis identifies explicit terms (such as those found in a conceptual analysis) and the patterns in terms of how they co-occur in a text. In other words, proximity analysis investigates the relationship between terms and aims to group these to extract themes and develop meaning.

Proximity analysis is typically utilised when you’re looking for hard facts rather than emotional, cultural, or contextual factors. For example, if you were to analyse a political speech, you may want to focus only on what has been said, rather than implications or hidden meanings. To do this, you would make use of explicit data, discounting any underlying meanings and implications of the speech.

Lastly, there’s cognitive mapping, which can be used in addition to, or along with, proximity analysis. Cognitive mapping involves taking different texts and comparing them in a visual format – i.e. a cognitive map. Typically, you’d use cognitive mapping in studies that assess changes in terms, definitions, and meanings over time. It can also serve as a way to visualise affect extraction or proximity analysis and is often presented in a form such as a graphic map.

To recap on the essentials, content analysis is a qualitative analysis method that focuses on recorded human artefacts . It involves both conceptual analysis (which is more numbers-based) and relational analysis (which focuses on the relationships between concepts and how they’re connected).

Need a helping hand?

3. When should you use content analysis?

Content analysis is a useful tool that provides insight into trends of communication . For example, you could use a discussion forum as the basis of your analysis and look at the types of things the members talk about as well as how they use language to express themselves. Content analysis is flexible in that it can be applied to the individual, group, and institutional level.

Content analysis is typically used in studies where the aim is to better understand factors such as behaviours, attitudes, values, emotions, and opinions . For example, you could use content analysis to investigate an issue in society, such as miscommunication between cultures. In this example, you could compare patterns of communication in participants from different cultures, which will allow you to create strategies for avoiding misunderstandings in intercultural interactions.

Another example could include conducting content analysis on a publication such as a book. Here you could gather data on the themes, topics, language use and opinions reflected in the text to draw conclusions regarding the political (such as conservative or liberal) leanings of the publication.

4. How to conduct a qualitative content analysis

Conceptual and relational content analysis differ in terms of their exact process ; however, there are some similarities. Let’s have a look at these first – i.e., the generic process:

• Recap on your research questions
• Undertake bracketing to identify biases
• Operationalise your variables and develop a coding scheme
• Code the data and undertake your analysis

Step 1 – Recap on your research questions

It’s always useful to begin a project with research questions , or at least with an idea of what you are looking for. In fact, if you’ve spent time reading this blog, you’ll know that it’s useful to recap on your research questions, aims and objectives when undertaking pretty much any research activity. In the context of content analysis, it’s difficult to know what needs to be coded and what doesn’t, without a clear view of the research questions.

For example, if you were to code a conversation focused on basic issues of social justice, you may be met with a wide range of topics that may be irrelevant to your research. However, if you approach this data set with the specific intent of investigating opinions on gender issues, you will be able to focus on this topic alone, which would allow you to code only what you need to investigate.

Step 2 – Reflect on your personal perspectives and biases

It’s vital that you reflect on your own pre-conception of the topic at hand and identify the biases that you might drag into your content analysis – this is called “ bracketing “. By identifying this upfront, you’ll be more aware of them and less likely to have them subconsciously influence your analysis.

For example, if you were to investigate how a community converses about unequal access to healthcare, it is important to assess your views to ensure that you don’t project these onto your understanding of the opinions put forth by the community. If you have access to medical aid, for instance, you should not allow this to interfere with your examination of unequal access.

Step 3 – Operationalise your variables and develop a coding scheme

Next, you need to operationalise your variables . But what does that mean? Simply put, it means that you have to define each variable or construct . Give every item a clear definition – what does it mean (include) and what does it not mean (exclude). For example, if you were to investigate children’s views on healthy foods, you would first need to define what age group/range you’re looking at, and then also define what you mean by “healthy foods”.

In combination with the above, it is important to create a coding scheme , which will consist of information about your variables (how you defined each variable), as well as a process for analysing the data. For this, you would refer back to how you operationalised/defined your variables so that you know how to code your data.

For example, when coding, when should you code a food as “healthy”? What makes a food choice healthy? Is it the absence of sugar or saturated fat? Is it the presence of fibre and protein? It’s very important to have clearly defined variables to achieve consistent coding – without this, your analysis will get very muddy, very quickly.

Step 4 – Code and analyse the data

The next step is to code the data. At this stage, there are some differences between conceptual and relational analysis.

As described earlier in this post, conceptual analysis looks at the existence and frequency of concepts, whereas a relational analysis looks at the relationships between concepts. For both types of analyses, it is important to pre-select a concept that you wish to assess in your data. Using the example of studying children’s views on healthy food, you could pre-select the concept of “healthy food” and assess the number of times the concept pops up in your data.

Here is where conceptual and relational analysis start to differ.

At this stage of conceptual analysis , it is necessary to decide on the level of analysis you’ll perform on your data, and whether this will exist on the word, phrase, sentence, or thematic level. For example, will you code the phrase “healthy food” on its own? Will you code each term relating to healthy food (e.g., broccoli, peaches, bananas, etc.) with the code “healthy food” or will these be coded individually? It is very important to establish this from the get-go to avoid inconsistencies that could result in you having to code your data all over again.

On the other hand, relational analysis looks at the type of analysis. So, will you use affect extraction? Proximity analysis? Cognitive mapping? A mix? It’s vital to determine the type of analysis before you begin to code your data so that you can maintain the reliability and validity of your research .

How to conduct conceptual analysis

First, let’s have a look at the process for conceptual analysis.

Once you’ve decided on your level of analysis, you need to establish how you will code your concepts, and how many of these you want to code. Here you can choose whether you want to code in a deductive or inductive manner. Just to recap, deductive coding is when you begin the coding process with a set of pre-determined codes, whereas inductive coding entails the codes emerging as you progress with the coding process. Here it is also important to decide what should be included and excluded from your analysis, and also what levels of implication you wish to include in your codes.

For example, if you have the concept of “tall”, can you include “up in the clouds”, derived from the sentence, “the giraffe’s head is up in the clouds” in the code, or should it be a separate code? In addition to this, you need to know what levels of words may be included in your codes or not. For example, if you say, “the panda is cute” and “look at the panda’s cuteness”, can “cute” and “cuteness” be included under the same code?

Once you’ve considered the above, it’s time to code the text . We’ve already published a detailed post about coding , so we won’t go into that process here. Once you’re done coding, you can move on to analysing your results. This is where you will aim to find generalisations in your data, and thus draw your conclusions .

How to conduct relational analysis

Now let’s return to relational analysis.

As mentioned, you want to look at the relationships between concepts . To do this, you’ll need to create categories by reducing your data (in other words, grouping similar concepts together) and then also code for words and/or patterns. These are both done with the aim of discovering whether these words exist, and if they do, what they mean.

Your next step is to assess your data and to code the relationships between your terms and meanings, so that you can move on to your final step, which is to sum up and analyse the data.

To recap, it’s important to start your analysis process by reviewing your research questions and identifying your biases . From there, you need to operationalise your variables, code your data and then analyse it.

5. What are the pros & cons of content analysis?

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

For example, with conceptual analysis, you can count the number of times that a term or a code appears in a dataset, which can be assessed from a quantitative standpoint. In addition to this, you can then use a qualitative approach to investigate the underlying meanings of these and relationships between them.

Content analysis is also unobtrusive and therefore poses fewer ethical issues than some other analysis methods. As the content you’ll analyse oftentimes already exists, you’ll analyse what has been produced previously, and so you won’t have to collect data directly from participants. When coded correctly, data is analysed in a very systematic and transparent manner, which means that issues of replicability (how possible it is to recreate research under the same conditions) are reduced greatly.

On the downside , qualitative research (in general, not just content analysis) is often critiqued for being too subjective and for not being scientifically rigorous enough. This is where reliability (how replicable a study is by other researchers) and validity (how suitable the research design is for the topic being investigated) come into play – if you take these into account, you’ll be on your way to achieving sound research results.

Recap: Qualitative content analysis

In this post, we’ve covered a lot of ground – click on any of the sections to recap:

If you have any questions about qualitative content analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your qualitative content analysis, be sure to book an initial consultation with one of our friendly Research Coaches.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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14 Comments

If I am having three pre-decided attributes for my research based on which a set of semi-structured questions where asked then should I conduct a conceptual content analysis or relational content analysis. please note that all three attributes are different like Agility, Resilience and AI.

Thank you very much. I really enjoyed every word.

please send me one/ two sample of content analysis

send me to any sample of qualitative content analysis as soon as possible

Many thanks for the brilliant explanation. Do you have a sample practical study of a foreign policy using content analysis?

1) It will be very much useful if a small but complete content analysis can be sent, from research question to coding and analysis. 2) Is there any software by which qualitative content analysis can be done?

Common software for qualitative analysis is nVivo, and quantitative analysis is IBM SPSS

Thank you. Can I have at least 2 copies of a sample analysis study as my reference?

Could you please send me some sample of textbook content analysis?

Can I send you my research topic, aims, objectives and questions to give me feedback on them?

please could you send me samples of content analysis?

Yes please send

really we enjoyed your knowledge thanks allot. from Ethiopia

can you please share some samples of content analysis(relational)? I am a bit confused about processing the analysis part

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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 » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Chapter 17. Content Analysis

Introduction.

Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or Facebook. Really, almost anything can be the “content” to be analyzed. This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] Qualitative content analysis (sometimes referred to as QCA) is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest—in other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue. This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis. It is also a nice segue between our data collection methods (e.g., interviewing, observation) chapters and chapters 18 and 19, whose focus is on coding, the primary means of data analysis for most qualitative data. In many ways, the methods of content analysis are quite similar to the method of coding.

Although the body of material (“content”) to be collected and analyzed can be nearly anything, most qualitative content analysis is applied to forms of human communication (e.g., media posts, news stories, campaign speeches, advertising jingles). The point of the analysis is to understand this communication, to systematically and rigorously explore its meanings, assumptions, themes, and patterns. Historical and archival sources may be the subject of content analysis, but there are other ways to analyze (“code”) this data when not overly concerned with the communicative aspect (see chapters 18 and 19). This is why we tend to consider content analysis its own method of data collection as well as a method of data analysis. Still, many of the techniques you learn in this chapter will be helpful to any “coding” scheme you develop for other kinds of qualitative data. Just remember that content analysis is a particular form with distinct aims and goals and traditions.

An Overview of the Content Analysis Process

The first step: selecting content.

Figure 17.2 is a display of possible content for content analysis. The first step in content analysis is making smart decisions about what content you will want to analyze and to clearly connect this content to your research question or general focus of research. Why are you interested in the messages conveyed in this particular content? What will the identification of patterns here help you understand? Content analysis can be fun to do, but in order to make it research, you need to fit it into a research plan.

Figure 17.1. A Non-exhaustive List of "Content" for Content Analysis

To take one example, let us imagine you are interested in gender presentations in society and how presentations of gender have changed over time. There are various forms of content out there that might help you document changes. You could, for example, begin by creating a list of magazines that are coded as being for “women” (e.g., Women’s Daily Journal ) and magazines that are coded as being for “men” (e.g., Men’s Health ). You could then select a date range that is relevant to your research question (e.g., 1950s–1970s) and collect magazines from that era. You might create a “sample” by deciding to look at three issues for each year in the date range and a systematic plan for what to look at in those issues (e.g., advertisements? Cartoons? Titles of articles? Whole articles?). You are not just going to look at some magazines willy-nilly. That would not be systematic enough to allow anyone to replicate or check your findings later on. Once you have a clear plan of what content is of interest to you and what you will be looking at, you can begin, creating a record of everything you are including as your content. This might mean a list of each advertisement you look at or each title of stories in those magazines along with its publication date. You may decide to have multiple “content” in your research plan. For each content, you want a clear plan for collecting, sampling, and documenting.

The Second Step: Collecting and Storing

Once you have a plan, you are ready to collect your data. This may entail downloading from the internet, creating a Word document or PDF of each article or picture, and storing these in a folder designated by the source and date (e.g., “ Men’s Health advertisements, 1950s”). Sølvberg ( 2021 ), for example, collected posted job advertisements for three kinds of elite jobs (economic, cultural, professional) in Sweden. But collecting might also mean going out and taking photographs yourself, as in the case of graffiti, street signs, or even what people are wearing. Chaise LaDousa, an anthropologist and linguist, took photos of “house signs,” which are signs, often creative and sometimes offensive, hung by college students living in communal off-campus houses. These signs were a focal point of college culture, sending messages about the values of the students living in them. Some of the names will give you an idea: “Boot ’n Rally,” “The Plantation,” “Crib of the Rib.” The students might find these signs funny and benign, but LaDousa ( 2011 ) argued convincingly that they also reproduced racial and gender inequalities. The data here already existed—they were big signs on houses—but the researcher had to collect the data by taking photographs.

In some cases, your content will be in physical form but not amenable to photographing, as in the case of films or unwieldy physical artifacts you find in the archives (e.g., undigitized meeting minutes or scrapbooks). In this case, you need to create some kind of detailed log (fieldnotes even) of the content that you can reference. In the case of films, this might mean watching the film and writing down details for key scenes that become your data. [2] For scrapbooks, it might mean taking notes on what you are seeing, quoting key passages, describing colors or presentation style. As you might imagine, this can take a lot of time. Be sure you budget this time into your research plan.

Researcher Note

A note on data scraping : Data scraping, sometimes known as screen scraping or frame grabbing, is a way of extracting data generated by another program, as when a scraping tool grabs information from a website. This may help you collect data that is on the internet, but you need to be ethical in how to employ the scraper. A student once helped me scrape thousands of stories from the Time magazine archives at once (although it took several hours for the scraping process to complete). These stories were freely available, so the scraping process simply sped up the laborious process of copying each article of interest and saving it to my research folder. Scraping tools can sometimes be used to circumvent paywalls. Be careful here!

The Third Step: Analysis

There is often an assumption among novice researchers that once you have collected your data, you are ready to write about what you have found. Actually, you haven’t yet found anything, and if you try to write up your results, you will probably be staring sadly at a blank page. Between the collection and the writing comes the difficult task of systematically and repeatedly reviewing the data in search of patterns and themes that will help you interpret the data, particularly its communicative aspect (e.g., What is it that is being communicated here, with these “house signs” or in the pages of Men’s Health ?).

The first time you go through the data, keep an open mind on what you are seeing (or hearing), and take notes about your observations that link up to your research question. In the beginning, it can be difficult to know what is relevant and what is extraneous. Sometimes, your research question changes based on what emerges from the data. Use the first round of review to consider this possibility, but then commit yourself to following a particular focus or path. If you are looking at how gender gets made or re-created, don’t follow the white rabbit down a hole about environmental injustice unless you decide that this really should be the focus of your study or that issues of environmental injustice are linked to gender presentation. In the second round of review, be very clear about emerging themes and patterns. Create codes (more on these in chapters 18 and 19) that will help you simplify what you are noticing. For example, “men as outdoorsy” might be a common trope you see in advertisements. Whenever you see this, mark the passage or picture. In your third (or fourth or fifth) round of review, begin to link up the tropes you’ve identified, looking for particular patterns and assumptions. You’ve drilled down to the details, and now you are building back up to figure out what they all mean. Start thinking about theory—either theories you have read about and are using as a frame of your study (e.g., gender as performance theory) or theories you are building yourself, as in the Grounded Theory tradition. Once you have a good idea of what is being communicated and how, go back to the data at least one more time to look for disconfirming evidence. Maybe you thought “men as outdoorsy” was of importance, but when you look hard, you note that women are presented as outdoorsy just as often. You just hadn’t paid attention. It is very important, as any kind of researcher but particularly as a qualitative researcher, to test yourself and your emerging interpretations in this way.

The Fourth and Final Step: The Write-Up

Only after you have fully completed analysis, with its many rounds of review and analysis, will you be able to write about what you found. The interpretation exists not in the data but in your analysis of the data. Before writing your results, you will want to very clearly describe how you chose the data here and all the possible limitations of this data (e.g., historical-trace problem or power problem; see chapter 16). Acknowledge any limitations of your sample. Describe the audience for the content, and discuss the implications of this. Once you have done all of this, you can put forth your interpretation of the communication of the content, linking to theory where doing so would help your readers understand your findings and what they mean more generally for our understanding of how the social world works. [3]

Analyzing Content: Helpful Hints and Pointers

Although every data set is unique and each researcher will have a different and unique research question to address with that data set, there are some common practices and conventions. When reviewing your data, what do you look at exactly? How will you know if you have seen a pattern? How do you note or mark your data?

Let’s start with the last question first. If your data is stored digitally, there are various ways you can highlight or mark up passages. You can, of course, do this with literal highlighters, pens, and pencils if you have print copies. But there are also qualitative software programs to help you store the data, retrieve the data, and mark the data. This can simplify the process, although it cannot do the work of analysis for you.

Qualitative software can be very expensive, so the first thing to do is to find out if your institution (or program) has a universal license its students can use. If they do not, most programs have special student licenses that are less expensive. The two most used programs at this moment are probably ATLAS.ti and NVivo. Both can cost more than $500 [4] but provide everything you could possibly need for storing data, content analysis, and coding. They also have a lot of customer support, and you can find many official and unofficial tutorials on how to use the programs’ features on the web. Dedoose, created by academic researchers at UCLA, is a decent program that lacks many of the bells and whistles of the two big programs. Instead of paying all at once, you pay monthly, as you use the program. The monthly fee is relatively affordable (less than $15), so this might be a good option for a small project. HyperRESEARCH is another basic program created by academic researchers, and it is free for small projects (those that have limited cases and material to import). You can pay a monthly fee if your project expands past the free limits. I have personally used all four of these programs, and they each have their pluses and minuses.

Regardless of which program you choose, you should know that none of them will actually do the hard work of analysis for you. They are incredibly useful for helping you store and organize your data, and they provide abundant tools for marking, comparing, and coding your data so you can make sense of it. But making sense of it will always be your job alone.

So let’s say you have some software, and you have uploaded all of your content into the program: video clips, photographs, transcripts of news stories, articles from magazines, even digital copies of college scrapbooks. Now what do you do? What are you looking for? How do you see a pattern? The answers to these questions will depend partially on the particular research question you have, or at least the motivation behind your research. Let’s go back to the idea of looking at gender presentations in magazines from the 1950s to the 1970s. Here are some things you can look at and code in the content: (1) actions and behaviors, (2) events or conditions, (3) activities, (4) strategies and tactics, (5) states or general conditions, (6) meanings or symbols, (7) relationships/interactions, (8) consequences, and (9) settings. Table 17.1 lists these with examples from our gender presentation study.

Table 17.1. Examples of What to Note During Content Analysis

One thing to note about the examples in table 17.1: sometimes we note (mark, record, code) a single example, while other times, as in “settings,” we are recording a recurrent pattern. To help you spot patterns, it is useful to mark every setting, including a notation on gender. Using software can help you do this efficiently. You can then call up “setting by gender” and note this emerging pattern. There’s an element of counting here, which we normally think of as quantitative data analysis, but we are using the count to identify a pattern that will be used to help us interpret the communication. Content analyses often include counting as part of the interpretive (qualitative) process.

In your own study, you may not need or want to look at all of the elements listed in table 17.1. Even in our imagined example, some are more useful than others. For example, “strategies and tactics” is a bit of a stretch here. In studies that are looking specifically at, say, policy implementation or social movements, this category will prove much more salient.

Another way to think about “what to look at” is to consider aspects of your content in terms of units of analysis. You can drill down to the specific words used (e.g., the adjectives commonly used to describe “men” and “women” in your magazine sample) or move up to the more abstract level of concepts used (e.g., the idea that men are more rational than women). Counting for the purpose of identifying patterns is particularly useful here. How many times is that idea of women’s irrationality communicated? How is it is communicated (in comic strips, fictional stories, editorials, etc.)? Does the incidence of the concept change over time? Perhaps the “irrational woman” was everywhere in the 1950s, but by the 1970s, it is no longer showing up in stories and comics. By tracing its usage and prevalence over time, you might come up with a theory or story about gender presentation during the period. Table 17.2 provides more examples of using different units of analysis for this work along with suggestions for effective use.

Table 17.2. Examples of Unit of Analysis in Content Analysis

Every qualitative content analysis is unique in its particular focus and particular data used, so there is no single correct way to approach analysis. You should have a better idea, however, of what kinds of things to look for and what to look for. The next two chapters will take you further into the coding process, the primary analytical tool for qualitative research in general.

Further Readings

Cidell, Julie. 2010. “Content Clouds as Exploratory Qualitative Data Analysis.” Area 42(4):514–523. A demonstration of using visual “content clouds” as a form of exploratory qualitative data analysis using transcripts of public meetings and content of newspaper articles.

Hsieh, Hsiu-Fang, and Sarah E. Shannon. 2005. “Three Approaches to Qualitative Content Analysis.” Qualitative Health Research 15(9):1277–1288. Distinguishes three distinct approaches to QCA: conventional, directed, and summative. Uses hypothetical examples from end-of-life care research.

Jackson, Romeo, Alex C. Lange, and Antonio Duran. 2021. “A Whitened Rainbow: The In/Visibility of Race and Racism in LGBTQ Higher Education Scholarship.” Journal Committed to Social Change on Race and Ethnicity (JCSCORE) 7(2):174–206.* Using a “critical summative content analysis” approach, examines research published on LGBTQ people between 2009 and 2019.

Krippendorff, Klaus. 2018. Content Analysis: An Introduction to Its Methodology . 4th ed. Thousand Oaks, CA: SAGE. A very comprehensive textbook on both quantitative and qualitative forms of content analysis.

Mayring, Philipp. 2022. Qualitative Content Analysis: A Step-by-Step Guide . Thousand Oaks, CA: SAGE. Formulates an eight-step approach to QCA.

Messinger, Adam M. 2012. “Teaching Content Analysis through ‘Harry Potter.’” Teaching Sociology 40(4):360–367. This is a fun example of a relatively brief foray into content analysis using the music found in Harry Potter films.

Neuendorft, Kimberly A. 2002. The Content Analysis Guidebook . Thousand Oaks, CA: SAGE. Although a helpful guide to content analysis in general, be warned that this textbook definitely favors quantitative over qualitative approaches to content analysis.

Schrier, Margrit. 2012. Qualitative Content Analysis in Practice . Thousand Okas, CA: SAGE. Arguably the most accessible guidebook for QCA, written by a professor based in Germany.

Weber, Matthew A., Shannon Caplan, Paul Ringold, and Karen Blocksom. 2017. “Rivers and Streams in the Media: A Content Analysis of Ecosystem Services.” Ecology and Society 22(3).* Examines the content of a blog hosted by National Geographic and articles published in The New York Times and the Wall Street Journal for stories on rivers and streams (e.g., water-quality flooding).

• There are ways of handling content analysis quantitatively, however. Some practitioners therefore specify qualitative content analysis (QCA). In this chapter, all content analysis is QCA unless otherwise noted. ↵
• Note that some qualitative software allows you to upload whole films or film clips for coding. You will still have to get access to the film, of course. ↵
• See chapter 20 for more on the final presentation of research. ↵
• . Actually, ATLAS.ti is an annual license, while NVivo is a perpetual license, but both are going to cost you at least $500 to use. Student rates may be lower. And don’t forget to ask your institution or program if they already have a software license you can use. ↵

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Research Design Review

A discussion of qualitative & quantitative research design, analyzability & a qualitative content analysis case study.

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 284-285).

Purpose & Scope The primary purpose of this primary qualitative content analysis study was to extend the existing literature on the portrayal of women’s roles in print media by examining the imagery and themes depicted of heterosexual college-educated women who leave the workforce to devote themselves to being stay-at-home mothers (a phenomenon referred to as “opting out”) across a wide, diverse range of print publications. More specifically, this research set out to investigate two areas of media coverage: the content (e.g., the women who are portrayed in the media and how they are described) and the context (e.g., the types of media and articles).

This study examined a 16-year period from 1988 to 2003. This 16-year period was chosen because 1988 was the earliest date on which the researchers had access to a searchable database for sampling, and 2003 was the year that the term “opting out” (referring to women leaving the workforce to become full-time mothers) became popular. The researchers identified 51 articles from 30 publications that represented a wide diversity of large-circulation print media. The researchers acknowledged that the sample “underrepresents articles appearing in small-town outlets” (p. 502).

Analyzability There are two aspects of the TQF Analyzability component — processing and verification. In terms of processing, the content data obtained by Kuperberg and Stone from coding revealed three primary patterns or themes in the depiction of women who opt out: “family first, child-centric”; “the mommy elite”; and “making choices.” The researchers discuss these themes at some length and support their findings by way of research literature and other references. In some instances, they report that their findings were in contrast to the literature (which presented an opportunity for future research in this area). Their final interpretation of the data includes their overall assertion that print media depict “traditional images of heterosexual women” (p. 510).

Important to the integrity of the analysis process, the researchers absorbed themselves in the sampled articles and, in doing so, identified inconsistencies in the research outcomes. For example, a careful reading of the articles revealed that many of the women depicted as stay-at-home mothers were actually employed in some form of paid work from home. The researchers also enriched the discussion of their findings by giving the reader some context relevant to the publications and articles. For example, they revealed that 45 of the 51 articles were from general interest newspapers or magazines, a fact that supports their research objective of analyzing print media that reach large, diverse audiences.

In terms of verification, the researchers performed a version of deviant case analysis in which they investigated contrary evidence to the assertion made by many articles that there is a growing trend in the proportion of women opting out. Citing research studies from the literature as well as actual trend data, the researchers stated that the articles’ claim that women were increasingly opting out had weak support.

Kuperberg, A., & Stone, P. (2008). The media depiction of women who opt out. Gender & Society , 22 (4), 497–517.

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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review

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• Published: 02 March 2022

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• Cecilia Silvestri   ORCID: orcid.org/0000-0003-2528-601X 1 ,
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The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector.

Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes.

The analysis of the results allowed us to classify the sample of articles in three main clusters (“Assessment-LCA,” “Best practice,” and “Decision-making”) and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability.

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Source: Authors’ elaboration. Notes: The graph shows the temporal distribution of the articles under analysis

Source: Authors’ elaborations. Notes: The graph shows the time distribution of articles from the three major journals

Source: Authors’ elaboration. Notes: The graph shows the composition of the sample according to the three clusters identified by the analysis

Source: Authors’ elaboration. Notes: The graph shows the distribution of articles over time by cluster

Source: Authors’ elaboration. Notes: The graph shows the network visualization

Source: Authors’ elaboration. Notes: The graph shows the overlay visualization

Source: Authors’ elaboration. Notes: The graph shows the classification of articles by scientific field

Source: Authors’ elaboration. Notes: Article classification based on their cluster to which they belong and scientific field

Source: Authors’ elaboration

Source: Authors’ elaboration. Notes: The graph shows the distribution of items over time based on TBL

Source: Authors’ elaboration. Notes: The graph shows the Pareto diagram highlighting the most used indicators in literature for measuring sustainability in the agri-food sector

Source: Authors’ elaboration. Notes: The graph shows the distribution over time of articles divided into conceptual and empirical

Source: Authors’ elaboration. Notes: The graph shows the classification of articles, divided into conceptual and empirical, in-depth analysis

Source: Authors’ elaboration. Notes: The graph shows the geographical distribution of the authors

Source: Authors’ elaboration. Notes: The graph shows the distribution of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: The graph shows the time distribution of publication of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement, according to the circular economy approach

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Silvestri, C., Silvestri, L., Piccarozzi, M. et al. Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review. Int J Life Cycle Assess (2022). https://doi.org/10.1007/s11367-022-02032-1

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• Published: 10 April 2024

“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.

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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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The influence of antenatal imaging on prenatal bonding in uncomplicated pregnancies: a mixed methods analysis

• Emily Skelton   ORCID: orcid.org/0000-0003-0132-7948 1 ,
• Daniel Cromb   ORCID: orcid.org/0000-0002-9814-8841 2 , 3 ,
• Alison Smith 3 ,
• Gill Harrison   ORCID: orcid.org/0000-0003-2795-8190 4 ,
• Mary Rutherford   ORCID: orcid.org/0000-0003-3361-1337 2 ,
• Christina Malamateniou   ORCID: orcid.org/0000-0002-2352-8575 1 &
• Susan Ayers   ORCID: orcid.org/0000-0002-6153-2460 5  

BMC Pregnancy and Childbirth volume  24 , Article number:  265 ( 2024 ) Cite this article

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Prenatal bonding describes the emotional connection expectant parents form to their unborn child. Research acknowledges the association between antenatal imaging and enhanced bonding, but the influencing factors are not well understood, particularly for fathers or when using advanced techniques like fetal magnetic resonance imaging (MRI). This study aimed to identify variables which may predict increased bonding after imaging.

First-time expectant parents (mothers = 58, fathers = 18) completed a two-part questionnaire (QualtricsXM™) about their expectations and experiences of ultrasound ( n  = 64) or fetal MRI ( n  = 12) scans in uncomplicated pregnancies. A modified version of the Prenatal Attachment Inventory (PAI) was used to measure bonding. Qualitative data were collected through open-ended questions. Multivariate linear regression models were used to identify significant parent and imaging predictors for bonding. Qualitative content analysis of free-text responses was conducted to further understand the predictors’ influences.

Bonding scores were significantly increased after imaging for mothers and fathers ( p  < 0.05). MRI-parents reported significantly higher bonding than ultrasound-parents ( p  = 0.02). In the first regression model of parent factors (adjusted R 2  = 0.17, F  = 2.88, p  < 0.01), employment status (β = -0.38, p  < 0.05) was a significant predictor for bonding post-imaging. The second model of imaging factors (adjusted R 2  = 0.19, F  = 3.85, p  < 0.01) showed imaging modality (β = -0.53), imaging experience (β = 0.42) and parental excitement after the scan (β = 0.29) were significantly ( p  < 0.05) associated with increased bonding. Seventeen coded themes were generated from the qualitative content analysis, describing how scans offered reassurance about fetal wellbeing and the opportunity to connect with the baby through quality interactions with imaging professionals. A positive scan experience helped parents to feel excited about parenthood. Fetal MRI was considered a superior modality to ultrasound.

Conclusions

Antenatal imaging provides reassurance of fetal development which affirms parents’ emotional investment in the pregnancy and supports the growing connection. Imaging professionals are uniquely positioned to provide parent-centred experiences which may enhance parental excitement and facilitate bonding.

Peer Review reports

Ultrasound is used to evaluate fetal viability, development and well-being, and to identify occasions where medical intervention during pregnancy or shortly after birth may improve post-natal outcomes [ 1 ]. Yet, its efficacy as an imaging tool can be compromised by inherent limitations including fetal lie, maternal body habitus and operator technique [ 2 ]. As scan acquisition methods advance, fetal magnetic resonance imaging (MRI) has become popular to complement ultrasound in prenatal diagnosis because it provides increased anatomical detail for some physical conditions [ 3 ]. However, the imaging procedure is markedly different, and pregnant women and people may experience anxiety because of loud MRI machine noises, claustrophobia whilst in the MRI scanner, and discomfort in lying still for an extended period of time [ 4 ]. Compared to ultrasound examinations which do not usually exceed 30-min in duration, fetal MRI appointments may be scheduled for 60-min (although not all of this time is devoted to image acquisition) [ 5 ].

In addition to medical value, psychological benefits of fetal imaging are reported for expectant parents in providing an opportunity to see and connect with their unborn baby before birth [ 6 ]. For the non-pregnant parent, scans are also an opportunity to engage with the pregnancy and provide companionship and support to partners [ 7 ]. Broadly, parent-fetal bonding refers to the emotional connection that expectant parents feel towards their unborn babies during pregnancy [ 8 ]. This definition acknowledges the unidirectional nature of the parent-to-fetal relationship and considers the construct of bonding as theoretically distinct from original conceptualisations of attachment which are characterised by a system of care-seeking and care-giving behaviours after birth [ 9 ]. Quality prenatal bonding is associated with parental wellbeing and positive behaviours during pregnancy (e.g., smoking cessation) that subsequently contribute to healthy infant brain and neurological development [ 10 ]. Prenatal bonding is also thought to predict postnatal attachment [ 11 ], and further links between parent-fetal bonding (particularly the paternal-fetal relationship) and the child’s cognitive and socio-economic development also highlight the importance of studying this construct [ 12 ]. However, terminology of bonding and attachment are often used interchangeably to reflect varying definitions in the literature and as such, different approaches are utilised in attempts to evaluate not only the strength of the bond itself [ 9 ], but also the effect of interventions designed to facilitate its development [ 13 ]. Subsequently, inconsistent methodological approaches and varying quality in existing research studies have produced conflicting findings [ 14 ].

Fetal ultrasound images are thought to facilitate parents’ connection to the baby by providing visual knowledge that can be used to further enhance mental representations of the imagined child [ 11 ]. A recent literature review including 23 studies concluded that parent-fetal bonding was enhanced following antenatal imaging [ 6 ]. In particular, the role of the sonographer (a healthcare professional who performs ultrasound scans) in creating a parent-centred scan experience was highlighted as an important factor to facilitate bonding. Expectant parents rely on sonographers not only to assess fetal health, but also to transform the medical entity captured within the acquired images into relatable individuals who they can interact with, and place in their own realities [ 15 ]. MRI images, like ultrasound, are also dependent on expert clinical interpretation [ 16 ], however, they are less familiar to expectant parents than ultrasound, and there is little understanding of how parents respond to these highly detailed anatomical visualisations of their unborn baby [ 6 ].

MRI is not currently part of the routine fetal screening pathway in England [ 17 ], but is used for more complex clinical investigations where ultrasound is inconclusive, or in research studies aiming to improve understanding of human development. Although the images produced are considered higher quality because they are not affected by the previously described limitations associated with ultrasound, it is unlikely that it will replace it due to increased financial cost and limited availability of specialist fetal MR imaging services [ 16 ]. This means that many studies reporting expectant parents’ experiences and perceptions of MRI are set in the context of a prenatal diagnosis where increased parental anxiety and distress may be a moderator of bonding [ 4 , 18 ]. They are also retrospective, therefore many variables or confounding factors are missing, or cannot be controlled for. Prospective research is required to further understand parental experiences and the potential influence of MRI on bonding. Additionally, research exploring the paternal-fetal bond is limited compared to maternal studies [ 19 ]. As fathers and partners are now increasingly involved in pregnancy [ 7 ], it is important to better understand their perceptions, experiences and individual needs around accessing antenatal care in order for services to be inclusive and supportive [ 20 ].

Based on other literature exploring bonding and scan experiences in pregnancy [ 6 ], it was hypothesised that parent-fetal bonding scores would increase after imaging. Therefore, this study aimed to further identify parental and scan variables which may be associated with enhanced parent-fetal bonding after ultrasound or MRI, and qualitatively explore how they may facilitate the developing connection.

The STROBE checklist was used to guide reporting [ 21 ]. A two-part questionnaire was developed for data collection, hosted on the Qualtrics XM™ platform ( www.qualtrics.com ).

Recruitment ran between October 2021-December 2022. First-time expectant parents (≥ 18 years) attending a London hospital for fetal imaging (routine ultrasound or research MRI) between 18–36 weeks gestation in uncomplicated pregnancies were eligible to participate. Convenience sampling was used; ultrasound-parents were identified by clinical staff following completion of their routine first trimester screening scan between 11 +2 –14 +1 weeks of pregnancy [ 17 ], and MRI-parents were identified by perinatal imaging researchers when booking their research MRI scan. An introductory email was sent to prospective parents containing links to the participant information video and electronic informed consent form, which was designed according to good practice recommendations [ 22 ]. Once recruited, participants were allocated a unique identification number which they used to access the questionnaire. Two weeks before the imaging appointment, the weblink to part one of the questionnaire (pre-imaging) was shared. The link to part two (post-imaging) was shared one week after the scan (Fig.  1 ). Reminders to complete the relevant parts of the questionnaire were sent at 7 and 14 days after they were initially shared.

Schedule of participation

Part one of the questionnaire contained four sections, and part two was composed of three (Fig.  2 ). Demographic information was only collected in the first part.

Questionnaire structure

Prenatal attachment inventory (PAI)

A modified version of the Prenatal Attachment Inventory (PAI) [ 8 ] was used to measure parent-fetal bonding. Gendered items were removed or rephrased so that both mothers and fathers could respond to the same questions (e.g. “I tell others what the baby does inside me” became “I tell others what the baby does inside the womb”). For each item, parents were asked to select a Likert-response of “Almost Never,” “Sometimes,” “Often,” or “Almost Always.” A value between 1–4 was allocated to each response, and the total PAI score was calculated. Higher scores are associated with a more developed bond [ 23 ], and in this 16-item PAI, the maximum possible score was 64. Good reliability of the modified PAI is previously established [ 24 , 25 ]. In this study, Cronbach’s alpha (α) was 0.90, indicating excellent internal consistency.

Psychological distress in participants was evaluated using the CORE-10 [ 26 ], which has been validated for use in the perinatal population [ 27 ]. Participants were asked to respond to 10-items using one of five Likert-responses ranging from “Not at all,” to “Most or all of the time” based on their experiences during the preceding week. Responses were allocated a value between 0–4 and combined. Total scores of ≥ 25 are associated with severe psychological distress [ 28 ]. Cronbach’s alpha (α) for the CORE-10 was 0.84.

Parental expectations, experience and reactions to antenatal imaging

Exisiting measures of parental expectations and experiences of antenatal imaging [ 29 ] were not suitable for the current study’s focus on bonding so a measure was specifically developed for use based on prior literature findings and research studies [ 6 , 25 ]. For statistical comparison, expectation and experience factors were matched (Fig.  3 ). An overall score was calculated from the total number of factors (maximum 5 score).

Matched questions to evaluate pre-imaging expectations and imaging experience

Rating scales (where 0 = not at all and 10 = extremely) were utilised for participants to report their reactions to imaging (anxiety and excitement prior to imaging, and anxiety, excitement, and satisfaction after imaging). Open-questions (e.g., What are you least looking forward to about your scan? What did you most enjoy about your scan?) were also included to further capture parental perspectives.

Representatives from UK-based support charities, Antenatal Results and Choices (ARC) and Fathers Reaching Out were invited to review the questionnaire and provide feedback regarding readability and usability. This resulted in minor amendments to the presentation (e.g., change of rating scale slider) for ease of use on a mobile device. Prior to launch, the questionnaire was piloted by parent volunteers ( n  = 7). The QualtricsXM™ platform contained instructions for navigating the questionnaire, including the use of directional tools to move between sections for editing. Participants could complete the questionnaire with no time limit enforced. The option to save and return to an incomplete questionnaire at a different time was also available.

Data analysis

Sample size for paired analysis was informed by a power calculation based on previous studies evaluating the change in maternal–fetal bonding after antenatal ultrasound [ 30 , 31 ]. From these, it was assumed that bonding scores may be increased by an average of 3-points. Using an alpha level of 0.05 and power of 80%, the minimum sample size required for this study was estimated as n = 70. Sample size for regression analysis was guided by published literature suggesting the number of subjects per independent variable should lie between 5–20 [ 32 ]. Therefore, it was aimed to include 10 subjects per variable in each model.

Quantitative data were analysed using Microsoft Excel (version 2008) and IBM SPSS Statistics (version 29). Frequencies and descriptive statistics including average scores for imaging expectations and experience, PAI and CORE-10 scores were calculated for each parent group. Kolmogorov–Smirnov tests indicated normally distributed data, therefore parametric statistical analyses were performed [ 33 ]. Independent and paired t-tests (assuming unequal variances where Levene’s statistic was significant) were used to compare means. Hedge’s g statistic (g) determined effect size. Cases were excluded from some analyses where paired data was unavailable. Two multivariate linear regression analyses were run to identify predictors significantly associated with enhanced bonding after imaging. Parent variables (e.g., parent demographics and social factors) were entered into the first model and scan variables (e.g., imaging modality, experience, and parental reactions) were entered into the second. Categorical variables were converted to binary-coded dummy variables (e.g., ethnicity became majority/white or minority/non-white) to enable their inclusion in the regression analysis whilst minimising the potential for overfitting in the models [ 34 ]. Statistical significance was determined at p  < 0.05.

Qualitative content analysis of free-text responses was undertaken to help explain findings of the regression models. This was chosen over more interpretative methods because the brevity of responses was not conducive to deep analysis [ 35 ]. A deductive coding system was developed ES (a sonographer with 12 years’ experience in obstetric ultrasound) using the significant predictors identified in the regression models as coding clusters [ 36 ]. Responses were first organised into clusters and abstracted into units of meaning. Identified units were recontextualised and grouped into initial coded themes and reviewed against the original data. Coded themes were refined before being checked against the coding clusters to ensure their appropriate classification [ 37 ]. To evaluate reliability of the coding system, re-coding of a randomised 10% of the qualitative responses was independently performed by DC (a paediatrician and clinical research fellow with 5 years’ experience of fetal MRI). Following this, minor changes to the coding descriptors were made for improved clarity. Inter-coder agreement on 10% of the content was 96% following resolution of discrepancies.

Ethical approval was received by the West of Scotland REC 3 (REC reference: 20/WS/0132, date of approval 12th November 2020) and School of Health and Psychological Sciences REC at City, University of London (REC reference: ETH1920-1680, date of approval 30th November 2020). Due to the sensitive nature of this research, only participants who were committed to continuing their pregnancy were approached to participate. The potential risk of parental anxiety caused by taking part was low, however a contact list of perinatal mental health support resources was shared after completing part one of the questionnaire. An emergency referral pathway was developed in conjunction with the local perinatal mental health team to provide urgent support for parents who scored highly for psychological distress although its use was never required.

All parents stated they were either the mother or father of the baby. A total of 76 expectant parents (58 mothers, 18 fathers) completed part one of the questionnaire. Of these, 64 had ultrasound and 12 had fetal MRI. Sixteen sets of parents were in a couple. Three parents did not respond to the invitation to complete part two, resulting in paired data for 73 parents (56 mothers, 17 fathers).

Mean maternal age was 32 (range = 23–39), and mean paternal age was 34 (range = 28–41). Most parents were educated to postgraduate degree level ( n  = 39, 51.3%), of white ethnicity ( n  = 57, 75.0%) and in full-time employment ( n  = 64, 84.2%). Sixteen parents (21.1%) disclosed a pre-existing physical health condition, and twenty (26.3%) reported receiving a previous diagnosis of, or support for a mental health condition (Table  1 ).

Fetal imaging was performed between October 2021-December 2022. Mean gestational age (GA) in weeks and days at the time of the scan was 21 +1 (range: 18 +6 –33 +2 ) for ultrasound and 27 +1 (range: 18 +4 –35 +4 ) for MRI.

Parent-fetal bonding (PAI)

Bonding was significantly increased in mothers ( p  < 0.001) and fathers ( p  = 0.04) after imaging. Mean increase was larger in mothers (4.71, g = -0.81) than fathers (3.06, g = -0.53). No significant differences in mean scores were observed between mothers and fathers pre or post-imaging (Table  2 ). MRI-parents had significantly higher bonding scores than ultrasound-parents, both before and after imaging. The pre-imaging mean difference in PAI was 7.25 ( p  = 0.01, g = -0.85). Post-imaging, the mean difference was 6.46 ( p  = 0.02, g = -0.74).

Predictors of bonding after imaging

Eight parent variables were entered into the first multivariate regression model (Table  3 ). This model was significant (adjusted R 2  = 0.17, F  = 2.88, p  < 0.01) and showed that employment status was significantly predictive of parent-fetal bonding after imaging (β = -0.38, p  < 0.05), with unemployed and part-time working parents scoring higher on the PAI than those in full-time work.

The second model was also significant (adjusted R 2  = 0.19, F  = 3.85, p  < 0.01). Three of the six imaging variables (Table  4 ) were significantly predictive of bonding. These were imaging modality type (β = -0.53, p  < 0.05), imaging experience (β = 0.42, p  < 0.05), and parental excitement after imaging (β = 0.29, p  = 0.02). Issues of multicollinearity were not indicated as variance inflation factors in the models were between 1.12–2.33 (tolerance = 0.54–0.87).

Parental expectations, experience, psychological distress and reactions to imaging

Pre vs. post-imaging.

Average CORE-10 scores in all parents (including those with a prior mental health condition) were < 10 which indicated low-level psychological distress (not of clinical concern). Mothers’ pre and post-scan CORE-10 scores were similar, however fathers’ scores were significantly decreased after imaging ( p  < 0.001). Anxiety significantly decreased after imaging in mothers ( p  < 0.001) and fathers ( p  = 0.01). Fathers’ post-imaging excitement was significantly higher ( p  = 0.01), although this increase was not observed in mothers. No significant difference between pre-scan expectation and post-scan experience score was noted for mothers or fathers (Table  5 ).

Mothers vs. fathers

Although mean values suggest low anxiety in both parents, it was still significantly ( p  < 0.001) higher in mothers (4.21, SD = 2.45) compared to fathers (2.39, SD = 1.29) pre-imaging. Post-imaging, the mean difference between fathers’ (9.12, SD = 1.05) and mothers’ excitement (7.63, SD = 2.29) was also significant ( p  < 0.001). Fathers’ post-imaging satisfaction (9.12, SD = 1.05) was also significantly higher than mothers’ (8.36, SD = 1.78) although the effect size was small ( p  = 0.04, g = 0.46). A final significant difference ( p  = 0.02) was noted between mothers’ and fathers’ post-imaging CORE-10 scores, with mothers scoring higher (8.38, SD = 5.94) than fathers (4.82, SD = 3.09). No significant differences in pre-imaging excitement, pre-imaging CORE-10 or post-imaging anxiety were observed between mothers and fathers (Table  6 ).

Ultrasound vs. MRI

There were very few differences between parents who had ultrasound or MRI. Ultrasound-parents had significantly higher pre-imaging expectation scores than MRI-parents ( p  = 0.01). Imaging experience scores between the modalities were also significantly different ( p  = 0.01), with ultrasound-parents scoring higher (4.75, SD = 0.47) than MRI-parents (3.75, SD = 1.14). No significant differences were observed between mean scores for anxiety, excitement, post-imaging satisfaction or CORE-10 in ultrasound-parents compared to MRI-parents.

Qualitative findings

Of the four statistically significant predictors, qualitative data relating to parental employment were not collected, therefore this was not included as a coding cluster in the content analysis. A fourth category (parent type) was developed to further explore perspectives of mothers and fathers. Seventeen coded themes were generated (Table  7 ), representing 78.05% of the content. Coded themes are presented by statistical importance as per the regression analyses.

Imaging modality

MRI-parents perceived the imaging technique as superior to ultrasound, however, in contrast to its importance in the regression analysis, it was not a high frequency theme in their open-text responses ( n  = 13, 1.04%).

Imaging experience

Parents regarded imaging as a tool to provide reassurance about fetal health ( n  = 174, 12.94%), although they were simultaneously anxious of the potential to receive unexpected news about a fetal anomaly or pregnancy complication ( n  = 111, 8.12%). Satisfaction in the experience was reported by parents who had their expectations for care adequately met ( n  = 84, 6.19%), which included feeling informed about the scan procedure. This was facilitated by positive interactions with radiographers and sonographers ( n  = 67, 4.40%), although the rushed “conveyor belt” experience was also described by some parents and identified as an area to address for improved provision of parent-centred care ( n  = 65, 4.63%). Discomfort in the scan procedure was reported for both modalities ( n  = 37, 2.92%). Ultrasound-mothers were uncomfortable because of transducer pressure on their abdomen, particularly if the fetal lie was unfavourable, and being scanned with a full bladder. MRI-mothers noted feelings of claustrophobia, loud scanner noises, and lying still for an extended period as causes of discomfort. Parental dissatisfaction was expressed in relation to hospital waiting times and COVID-19 infection control measures which were unsupportive of partner attendance ( n  = 37, 2.47%), as well as a lack of information about the scan ( n  = 19, 1.15%). Increased options for imaging extras including choosing souvenir photos, recording video clips, having 3-Dimensional ultrasound offered as standard, and receiving MRI images immediately after the scan were suggested as further means to improve experiences ( n  = 25, 1.63%).

Parent excitement

References to “seeing baby” were most frequently observed in the free-text responses ( n  = 197, 13.43%). Parents enjoyed visualising fetal movement and cardiac activity during scans as it provided reassurance. Images helped parents to personify the fetus, creating a sense of familiarity that could be further intensified by learning the fetal sex ( n  = 78, 3.77%). For some parents, the scan marked a pivotal moment to accept the reality of pregnancy and embrace the transition to parenthood ( n  = 66, 4.77%). The scan experience was perceived by both parents as beneficial, particularly for fathers in enhancing their emotional connection with the baby, and strengthening the partner relationship ( n  = 31, 1.84%).

Parent type

Many parents reported that in the absence of any physical experience of pregnancy, imaging provided a unique and exciting opportunity for fathers’ engagement ( n  = 62, 4.78%). Mothers reported greater apprehension prior to scans due to the possibility of an unexpected finding, and actively supressed excitement until receiving confirmation of fetal health ( n  = 41, 3.05%). Mothers’ anxiety was also created by assuming greater responsibility for the scan or pregnancy outcome ( n  = 13, 0.92%), for example fetal sex or position.

In this study, parent-fetal bonding scores were significantly increased following imaging in both parents which is consistent with existing literature [ 6 ]. However, in contrast to other studies [ 25 , 38 , 39 , 40 ], bonding scores were not observed to be significantly different between mothers and fathers. Four variables were identified as significant predictors of parent-fetal bonding after imaging: scores were significantly higher in parents who had MRI, who scored their imaging experience and excitement levels higher, and who were not in full-time employment. Parental excitement in visualising their baby and the positive experience of receiving confirmation of fetal health were the most frequent references in the qualitative content analysis.

Interpretation

Many parents regarded imaging as a tool for reassurance of fetal development and wellbeing, and, mothers in particular, described how they attempted to supress excitement about the pregnancy until receiving confirmation of fetal health [ 41 ]. Whilst it has been suggested that conceptualisations of the “tentative pregnancy” may indicate detachment from the fetus in parents’ reluctance to embrace the developing bond [ 42 ], it has been argued that this response (often perceived as anxiety or worry about a possible unexpected physical condition or pregnancy loss) actually demonstrates the presence of this connection as fear that the imagined baby may not become reality [ 14 ].

The high frequency of references made to ‘seeing baby’ shows how scans provided powerful visual evidence used by parents to further validate assurances of fetal health offered by healthcare professionals [ 43 ]. However, in addition to reassurance, the images could be regarded as a source of uncertainty, creating anxiety if parents are not guided in how to interpret them [ 1 ]. Further uncertainty may also be created by communication around the limitations of prenatal screening [ 44 ], particularly if acquired images are low-quality [ 2 ]. Anxiety was significantly decreased for both parents after imaging, suggesting scans helped to mitigate this reaction. Additionally, some parents may not identify as anxious before the scan, however, expressing relief post-imaging may imply suppressed anxiety [ 29 ]. It has been suggested that the need for reassurance arises from anxiety created by the scan itself and uncertainties in fetal screening [ 45 ]. This may partly explain why parents perceived MRI as superior, due to its reputation as a more objective, diagnostic modality [ 46 ]. The wider field-of-view also enables parents to visualise the whole fetus instead of a series of 2-Dimensional cross-sectional images. However, as with ultrasound, MRI images require skilful interpretation, which is dependent on a clinician’s specialist knowledge and experience [ 16 ], therefore it may not actually be considered completely objective.

Other explanations may be offered to further understand the association between MRI and higher bonding scores. First, it could be argued that as these scans occurred at a more advanced GA (and these parents would have already received reassurance about fetal health from routine ultrasound screening scans) their emotional connection was more developed [ 47 ]. However, although higher MRI bonding scores were consistently noted compared to ultrasound, GA was not found to be a significant predictor in the regression analysis. Secondly, it must be acknowledged that unlike ultrasound, MRI scans were performed for research purposes. Parents may volunteer for pregnancy research because of its perceived benefits to the fetus [ 48 ], which suggests emotional investment through demonstration of responsible parenting [ 49 ]. Alternatively, parents experiencing a deeper connection may have been more motivated at the opportunity to see their baby again [ 50 ].

The findings also suggest how parental excitement is increased after imaging, and why this may help to enhance bonding. Parents reported feeling excited to ‘see the baby’ and ‘hear the heartbeat’. Visual and audial scan cues may substantiate fetal presence, and facilitate growing tangibility of the baby [ 14 ]. After scanning, some parents remarked how the pregnancy felt more ‘real’ and expressed excitement imagining the baby in their lives. This may highlight scans as a’trigger moment’ where the bond is initiated or intensified [ 19 ], and parents are prompted to engage with their new caregiving role [ 51 ]. For some, the scan was an opportunity to learn the fetal sex, which further contributed to feelings of closeness to the baby and excitement. Yet, it has been argued that knowing the fetal sex may actually be problematic for bonding [ 14 ], particularly if it does not align to parental preferences, or is inaccurate, as this mismatch in expectations requires parents to adjust their existing mental depictions [ 52 ].

Regardless of imaging modality, fathers’ excitement was noted to be consistently and significantly higher than mothers. Whilst some free-text responses alluded to fathers lack of awareness or anxiety for unexpected news to explain this [ 53 ], it may also be considered that fathers were increasingly excited about the opportunity to be involved in an aspect of antenatal care [ 7 ]. Fathers and partners are more likely to attend ultrasound scans than other antenatal checks [ 54 ]. Nevertheless, being present does not guarantee a positive experience for either parent, especially if healthcare professionals fail to fully acknowledge the partner’s role [ 55 ]. Pregnancy is regarded as a psychologically demanding time for fathers transitioning into their parental role [ 56 ], and conflicting emotions experienced during this time may be associated with feelings of chaos or loss of control [ 57 ].

It has been suggested that healthcare professionals are not adequately trained to engage with partners [ 58 ] which leads to their exclusion from care interactions [ 59 , 60 ] and further contributes to feelings of confusion and isolation [ 61 ]. In this study, COVID-19 infection control measures in the ultrasound department requiring fathers to wait in a separate area of the hospital to their partners created stress for both parents. This reflects findings reported in relation to the COVID-19 pandemic when partners were temporarily restricted from attending scans [ 25 ]. As they do not physically experience pregnancy, providing support through companionship is thought to be a key aspect of how expectant fathers conceptualise their role during the prenatal period [ 19 ]. Being unable to fulfil this role reinforces feelings of inadequacy, which can negatively affect the sense of connection to the pregnancy [ 62 ]. Partner inclusion is important for prenatal bonding and to support maternal emotional wellbeing [ 63 ], therefore, healthcare professionals should make efforts to involve partners by acknowledging the importance of their presence [ 57 ], providing father-focused information [ 20 ], and directing conversation to both parents [ 64 ]. ‘Interactions with healthcare professionals’ was developed to highlight the integral role of the imaging professional in facilitating good communication, which contributed to positive parental experiences and reduced anxiety. Thoroughly explaining the scanning process and images, being open to questions and not rushing through the appointment were identified as central to parent-centred care. Indeed, previous literature has reported improved satisfaction in the scan experience associated with increased feedback from healthcare professionals [ 29 , 65 ]. However, recent research suggests that moral injury and occupational burnout experienced by UK obstetric sonographers because of the COVID-19 pandemic may present substantial challenges to the provision of parent-centred care [ 66 , 67 ].

Whilst the influence of parental employment (e.g., unemployed or part-time working) to enhance bonding was not further qualified, it may be that parents in full-time employment have reduced cognitive capacity to engage in imaginative practices which are essential to facilitate the developing bond, as they may be preoccupied with procedural and operational aspects of their work [ 68 ]. A similar explanation relating to cognitive capacity was proffered pertaining to the negative effect of anxiety related to COVID-19 pandemic on parent-fetal bonding [ 69 ], where it was argued that increasing preoccupation with pandemic-related anxiety in mothers decreased their capability to think about the baby [ 70 ].

Clinical implications

Although various scales attempt to quantify parent-fetal bonding [ 71 ], the clinical use of this metric is uncertain. Whilst higher scores are typically considered to reflect a more developed bond, no optimal value has been reported [ 23 ]. A positive correlation between bonding and GA has been previously observed [ 72 ], and supports the theory of key ‘trigger moments’ throughout pregnancy to intensify the bond [ 19 ]. However, this implies that bonding is a linear process, which may not be reflective of all parents’ experiences. Instead, it has been suggested that even if ‘low’ bonding scores are recorded by parents earlier in the pregnancy, their developing connection is likely to be comparable with other parents at the end of the pregnancy [ 14 ]. As such, it is possible to inaccurately label a prenatal bond as dysfunctional, which may cause expectant parents to feel inadequate, and thus have substantial implications, not only for the developing bond, but postnatal infant attachment [ 73 ]. In addition, it may be argued that the development of an optimal value based on self-reported scores would not adequately reflect the theoretical complexity of the prenatal bonding construct, and therefore should not be considered in isolation to guide the provision of enhanced support for expectant parents. Thus, it is recommended in the first instance that a parent-centred approach to care which recognises and meets the individual needs of expectant parents is adopted within fetal imaging services to facilitate supportive experiences that may, in turn, promote enhanced parent-fetal bonding. Indeed, studies reporting the positive effect of healthcare consultations on prenatal bonding further reflect the findings of this study [ 74 , 75 ], and suggest that the care interaction experienced during fetal imaging may be an important moderator to consider in the antenatal setting [ 76 ].

Strengths and limitations

Prospective data collection facilitated engagement with different parent groups and modalities to enable focused comparisons to be made. Additionally, many studies evaluating parent-fetal bonding after imaging are purely quantitative; in this study, free-text responses provided qualitative context to extend the statistical findings [ 77 ]. A further strength was the use of validated instruments for data collection in all parents which permitted direct comparisons to be made between parent groups. However, self-reported bonding scores may be limited by social desirability bias [ 78 ]. In this context, parents completing the questionnaire may have altered their responses to achieve a higher score [ 73 ]. It has also been suggested that fathers may not disclose negative feelings if they think doing so may detract professional care and attention from their partner, or if they do not believe they are entitled to [ 79 ]. Another limitation was the predominance of ultrasound-mothers in the sample. Lack of fathers’ engagement in pregnancy research is acknowledged [ 80 ], and despite targeted efforts to recruit fathers into this study, numbers are low, reflecting the need to further improve approaches. In addition, recruitment of eligible MRI-parents was affected by continued disruption of research studies after the peak of the COVID-19 pandemic [ 81 ]. Although the pre-determined target sample size of n = 70 was achieved, it is likely that a greater number of participants would provide further power in the quantitative findings [ 82 ]. However, it should be noted that in addition to the challenges experienced in recruiting fathers into antenatal research, as a relatively new imaging modality in pregnancy, the provision of fetal MRI in the UK is limited. Thus, these initial findings serve to provide preliminary insight into expectant parents’ experiences of this technology and future work should seek to build on this. Enlarging the dataset and extending the sample population would also be beneficial to include greater representation of parents (including same-sex couples or non-binary parents), ethnicities and educational level.

A detailed understanding of the influence of antenatal imaging on the developing parent-fetal bond is essential to ensure the provision of supportive and inclusive care for expectant parents accessing imaging services. This work extends existing knowledge by directly comparing mothers and fathers, and introduces new insights related to the use of fetal MRI in uncomplicated pregnancies. Bonding scores were significantly increased in both parents after imaging, however no differences between mothers and fathers were observed. Bonding was greater in parents after MRI compared to ultrasound although this may reflect the more developed emotional connection at later GAs. Parental excitement and experience were also identified as important variables, and qualitative analysis suggested they may be influenced by the professional conduct of imaging professionals during the scan. Effective communication helped parents to interpret scan images and offered reassurance of fetal wellbeing, contributing to a positive experience. Visualisation of the fetus provided evidence of its presence, which intensified parents’ sense of connection to the baby and increased excitement in imagining future parenthood. Imaging professionals should therefore adopt an informed, parent-centred approach to care to best support expectant parents.

Availability of data and materials

There are ethical restrictions on public sharing of this study’s dataset because of limited anonymity. However, a minimum dataset will be made available on reasonable request to the lead author ([email protected]) and institutional research ethics committee ([email protected]).

Abbreviations

Gestational age

Magnetic resonance imaging

Prenatal attachment inventory

National Institute for Health and Care Excellence. Overview | Antenatal care for uncomplicated pregnancies | Guidance | NICE. 2019. https://www.nice.org.uk/guidance/cg62 .

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Acknowledgements

The authors would like to thank all parents who participated in this study. Thanks are also extended to ARC, Fathers Reaching out, and parent volunteers for reviewing the study protocol and questionnaire. Imogen Desforges and Chidinma Iheanetuoguejiofor are also acknowledged for their support in identifying prospective participants.

This work was funded by the College of Radiographers' Doctoral Fellowship Award (DF017) and the School of Health and Psychological Sciences at City, University of London. Funding from the City Radiography Research Fund has been instrumental for dissemination. The funders were not involved in the design, analysis, or writing of this manuscript.

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ES: Conceptualisation, Methodology, Formal analysis, Investigation, Writing – Original draft, Writing – Review & Editing, Funding acquisition, Project administration; DC: Resources, Validation, Writing – Review & Editing; AS: Resources, Writing – Review & Editing; GH: Writing – Review & Editing; MR: Resources, Writing – Review & Editing, Supervision; CM: Conceptualisation, Methodology, Writing – Review & Editing, Funding acquisition, Supervision; SA: Conceptualisation, Methodology, Writing – Review & Editing, Supervision. All authors reviewed and approved the final manuscript.

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This study was granted approval from the NHS West of Scotland REC 3 (reference: 20/WS/0132, approval date 12th November 2020) and School of Health and Psychological Sciences REC at City, University of London (reference: ETH1920-1680, approval date: 30th November 2020).

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Skelton, E., Cromb, D., Smith, A. et al. The influence of antenatal imaging on prenatal bonding in uncomplicated pregnancies: a mixed methods analysis. BMC Pregnancy Childbirth 24 , 265 (2024). https://doi.org/10.1186/s12884-024-06469-0

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A hands-on guide to doing content analysis

Christen erlingsson.

a Department of Health and Caring Sciences, Linnaeus University, Kalmar 391 82, Sweden

Petra Brysiewicz

b School of Nursing & Public Health, University of KwaZulu-Natal, Durban 4041, South Africa

Associated Data

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including the emergency care context in Africa. Novice qualitative researchers are often daunted by the prospect of qualitative data analysis and thus may experience much difficulty in the data analysis process. Our objective with this manuscript is to provide a practical hands-on example of qualitative content analysis to aid novice qualitative researchers in their task.

African relevance

• • Qualitative research is useful to deepen the understanding of the human experience.
• • Novice qualitative researchers may benefit from this hands-on guide to content analysis.
• • Practical tips and data analysis templates are provided to assist in the analysis process.

Introduction

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including emergency care research. An increasing number of health researchers are currently opting to use various qualitative research approaches in exploring and describing complex phenomena, providing textual accounts of individuals’ “life worlds”, and giving voice to vulnerable populations our patients so often represent. Many articles and books are available that describe qualitative research methods and provide overviews of content analysis procedures [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Some articles include step-by-step directions intended to clarify content analysis methodology. What we have found in our teaching experience is that these directions are indeed very useful. However, qualitative researchers, especially novice researchers, often struggle to understand what is happening on and between steps, i.e., how the steps are taken.

As research supervisors of postgraduate health professionals, we often meet students who present brilliant ideas for qualitative studies that have potential to fill current gaps in the literature. Typically, the suggested studies aim to explore human experience. Research questions exploring human experience are expediently studied through analysing textual data e.g., collected in individual interviews, focus groups, documents, or documented participant observation. When reflecting on the proposed study aim together with the student, we often suggest content analysis methodology as the best fit for the study and the student, especially the novice researcher. The interview data are collected and the content analysis adventure begins. Students soon realise that data based on human experiences are complex, multifaceted and often carry meaning on multiple levels.

For many novice researchers, analysing qualitative data is found to be unexpectedly challenging and time-consuming. As they soon discover, there is no step-wise analysis process that can be applied to the data like a pattern cutter at a textile factory. They may become extremely annoyed and frustrated during the hands-on enterprise of qualitative content analysis.

The novice researcher may lament, “I’ve read all the methodology but don’t really know how to start and exactly what to do with my data!” They grapple with qualitative research terms and concepts, for example; differences between meaning units, codes, categories and themes, and regarding increasing levels of abstraction from raw data to categories or themes. The content analysis adventure may now seem to be a chaotic undertaking. But, life is messy, complex and utterly fascinating. Experiencing chaos during analysis is normal. Good advice for the qualitative researcher is to be open to the complexity in the data and utilise one’s flow of creativity.

Inspired primarily by descriptions of “conventional content analysis” in Hsieh and Shannon [3] , “inductive content analysis” in Elo and Kyngäs [5] and “qualitative content analysis of an interview text” in Graneheim and Lundman [1] , we have written this paper to help the novice qualitative researcher navigate the uncertainty in-between the steps of qualitative content analysis. We will provide advice and practical tips, as well as data analysis templates, to attempt to ease frustration and hopefully, inspire readers to discover how this exciting methodology contributes to developing a deeper understanding of human experience and our professional contexts.

Overview of qualitative content analysis

Synopsis of content analysis.

A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes is a process of further abstraction of data at each step of the analysis; from the manifest and literal content to latent meanings ( Fig. 1 and Table 1 ).

Example of analysis leading to higher levels of abstraction; from manifest to latent content.

Glossary of terms as used in this hands-on guide to doing content analysis. *

The initial step is to read and re-read the interviews to get a sense of the whole, i.e., to gain a general understanding of what your participants are talking about. At this point you may already start to get ideas of what the main points or ideas are that your participants are expressing. Then one needs to start dividing up the text into smaller parts, namely, into meaning units. One then condenses these meaning units further. While doing this, you need to ensure that the core meaning is still retained. The next step is to label condensed meaning units by formulating codes and then grouping these codes into categories. Depending on the study’s aim and quality of the collected data, one may choose categories as the highest level of abstraction for reporting results or you can go further and create themes [1] , [2] , [3] , [5] , [8] .

Content analysis as a reflective process

You must mould the clay of the data , tapping into your intuition while maintaining a reflective understanding of how your own previous knowledge is influencing your analysis, i.e., your pre-understanding. In qualitative methodology, it is imperative to vigilantly maintain an awareness of one’s pre-understanding so that this does not influence analysis and/or results. This is the difficult balancing task of keeping a firm grip on one’s assumptions, opinions, and personal beliefs, and not letting them unconsciously steer your analysis process while simultaneously, and knowingly, utilising one’s pre-understanding to facilitate a deeper understanding of the data.

Content analysis, as in all qualitative analysis, is a reflective process. There is no “step 1, 2, 3, done!” linear progression in the analysis. This means that identifying and condensing meaning units, coding, and categorising are not one-time events. It is a continuous process of coding and categorising then returning to the raw data to reflect on your initial analysis. Are you still satisfied with the length of meaning units? Do the condensed meaning units and codes still “fit” with each other? Do the codes still fit into this particular category? Typically, a fair amount of adjusting is needed after the first analysis endeavour. For example: a meaning unit might need to be split into two meaning units in order to capture an additional core meaning; a code modified to more closely match the core meaning of the condensed meaning unit; or a category name tweaked to most accurately describe the included codes. In other words, analysis is a flexible reflective process of working and re-working your data that reveals connections and relationships. Once condensed meaning units are coded it is easier to get a bigger picture and see patterns in your codes and organise codes in categories.

Content analysis exercise

The synopsis above is representative of analysis descriptions in many content analysis articles. Although correct, such method descriptions still do not provide much support for the novice researcher during the actual analysis process. Aspiring to provide guidance and direction to support the novice, a practical example of doing the actual work of content analysis is provided in the following sections. This practical example is based on a transcribed interview excerpt that was part of a study that aimed to explore patients’ experiences of being admitted into the emergency centre ( Fig. 2 ).

Excerpt from interview text exploring “Patient’s experience of being admitted into the emergency centre”

This content analysis exercise provides instructions, tips, and advice to support the content analysis novice in a) familiarising oneself with the data and the hermeneutic spiral, b) dividing up the text into meaning units and subsequently condensing these meaning units, c) formulating codes, and d) developing categories and themes.

Familiarising oneself with the data and the hermeneutic spiral

An important initial phase in the data analysis process is to read and re-read the transcribed interview while keeping your aim in focus. Write down your initial impressions. Embrace your intuition. What is the text talking about? What stands out? How did you react while reading the text? What message did the text leave you with? In this analysis phase, you are gaining a sense of the text as a whole.

You may ask why this is important. During analysis, you will be breaking down the whole text into smaller parts. Returning to your notes with your initial impressions will help you see if your “parts” analysis is matching up with your first impressions of the “whole” text. Are your initial impressions visible in your analysis of the parts? Perhaps you need to go back and check for different perspectives. This is what is referred to as the hermeneutic spiral or hermeneutic circle. It is the process of comparing the parts to the whole to determine whether impressions of the whole verify the analysis of the parts in all phases of analysis. Each part should reflect the whole and the whole should be reflected in each part. This concept will become clearer as you start working with your data.

Dividing up the text into meaning units and condensing meaning units

You have now read the interview a number of times. Keeping your research aim and question clearly in focus, divide up the text into meaning units. Located meaning units are then condensed further while keeping the central meaning intact ( Table 2 ). The condensation should be a shortened version of the same text that still conveys the essential message of the meaning unit. Sometimes the meaning unit is already so compact that no further condensation is required. Some content analysis sources warn researchers against short meaning units, claiming that this can lead to fragmentation [1] . However, our personal experience as research supervisors has shown us that a greater problem for the novice is basing analysis on meaning units that are too large and include many meanings which are then lost in the condensation process.

Suggestion for how the exemplar interview text can be divided into meaning units and condensed meaning units ( condensations are in parentheses ).

Formulating codes

The next step is to develop codes that are descriptive labels for the condensed meaning units ( Table 3 ). Codes concisely describe the condensed meaning unit and are tools to help researchers reflect on the data in new ways. Codes make it easier to identify connections between meaning units. At this stage of analysis you are still keeping very close to your data with very limited interpretation of content. You may adjust, re-do, re-think, and re-code until you get to the point where you are satisfied that your choices are reasonable. Just as in the initial phase of getting to know your data as a whole, it is also good to write notes during coding on your impressions and reactions to the text.

Suggestions for coding of condensed meaning units.

Developing categories and themes

The next step is to sort codes into categories that answer the questions who , what , when or where? One does this by comparing codes and appraising them to determine which codes seem to belong together, thereby forming a category. In other words, a category consists of codes that appear to deal with the same issue, i.e., manifest content visible in the data with limited interpretation on the part of the researcher. Category names are most often short and factual sounding.

In data that is rich with latent meaning, analysis can be carried on to create themes. In our practical example, we have continued the process of abstracting data to a higher level, from category to theme level, and developed three themes as well as an overarching theme ( Table 4 ). Themes express underlying meaning, i.e., latent content, and are formed by grouping two or more categories together. Themes are answering questions such as why , how , in what way or by what means? Therefore, theme names include verbs, adverbs and adjectives and are very descriptive or even poetic.

Suggestion for organisation of coded meaning units into categories and themes.

Some reflections and helpful tips

Understand your pre-understandings.

While conducting qualitative research, it is paramount that the researcher maintains a vigilance of non-bias during analysis. In other words, did you remain aware of your pre-understandings, i.e., your own personal assumptions, professional background, and previous experiences and knowledge? For example, did you zero in on particular aspects of the interview on account of your profession (as an emergency doctor, emergency nurse, pre-hospital professional, etc.)? Did you assume the patient’s gender? Did your assumptions affect your analysis? How about aspects of culpability; did you assume that this patient was at fault or that this patient was a victim in the crash? Did this affect how you analysed the text?

Staying aware of one’s pre-understandings is exactly as difficult as it sounds. But, it is possible and it is requisite. Focus on putting yourself and your pre-understandings in a holding pattern while you approach your data with an openness and expectation of finding new perspectives. That is the key: expect the new and be prepared to be surprised. If something in your data feels unusual, is different from what you know, atypical, or even odd – don’t by-pass it as “wrong”. Your reactions and intuitive responses are letting you know that here is something to pay extra attention to, besides the more comfortable condensing and coding of more easily recognisable meaning units.

Use your intuition

Intuition is a great asset in qualitative analysis and not to be dismissed as “unscientific”. Intuition results from tacit knowledge. Just as tacit knowledge is a hallmark of great clinicians [11] , [12] ; it is also an invaluable tool in analysis work [13] . Literally, take note of your gut reactions and intuitive guidance and remember to write these down! These notes often form a framework of possible avenues for further analysis and are especially helpful as you lift the analysis to higher levels of abstraction; from meaning units to condensed meaning units, to codes, to categories and then to the highest level of abstraction in content analysis, themes.

Aspects of coding and categorising hard to place data

All too often, the novice gets overwhelmed by interview material that deals with the general subject matter of the interview, but doesn’t seem to answer the research question. Don’t be too quick to consider such text as off topic or dross [6] . There is often data that, although not seeming to match the study aim precisely, is still important for illuminating the problem area. This can be seen in our practical example about exploring patients’ experiences of being admitted into the emergency centre. Initially the participant is describing the accident itself. While not directly answering the research question, the description is important for understanding the context of the experience of being admitted into the emergency centre. It is very common that participants will “begin at the beginning” and prologue their narratives in order to create a context that sets the scene. This type of contextual data is vital for gaining a deepened understanding of participants’ experiences.

In our practical example, the participant begins by describing the crash and the rescue, i.e., experiences leading up to and prior to admission to the emergency centre. That is why we have chosen in our analysis to code the condensed meaning unit “Ambulance staff looked worried about all the blood” as “In the ambulance” and place it in the category “Reliving the rescue”. We did not choose to include this meaning unit in the categories specifically about admission to the emergency centre itself. Do you agree with our coding choice? Would you have chosen differently?

Another common problem for the novice is deciding how to code condensed meaning units when the unit can be labelled in several different ways. At this point researchers usually groan and wish they had thought to ask one of those classic follow-up questions like “Can you tell me a little bit more about that?” We have examples of two such coding conundrums in the exemplar, as can be seen in Table 3 (codes we conferred on) and Table 4 (codes we reached consensus on). Do you agree with our choices or would you have chosen different codes? Our best advice is to go back to your impressions of the whole and lean into your intuition when choosing codes that are most reasonable and best fit your data.

A typical problem area during categorisation, especially for the novice researcher, is overlap between content in more than one initial category, i.e., codes included in one category also seem to be a fit for another category. Overlap between initial categories is very likely an indication that the jump from code to category was too big, a problem not uncommon when the data is voluminous and/or very complex. In such cases, it can be helpful to first sort codes into narrower categories, so-called subcategories. Subcategories can then be reviewed for possibilities of further aggregation into categories. In the case of a problematic coding, it is advantageous to return to the meaning unit and check if the meaning unit itself fits the category or if you need to reconsider your preliminary coding.

It is not uncommon to be faced by thorny problems such as these during coding and categorisation. Here we would like to reiterate how valuable it is to have fellow researchers with whom you can discuss and reflect together with, in order to reach consensus on the best way forward in your data analysis. It is really advantageous to compare your analysis with meaning units, condensations, coding and categorisations done by another researcher on the same text. Have you identified the same meaning units? Do you agree on coding? See similar patterns in the data? Concur on categories? Sometimes referred to as “researcher triangulation,” this is actually a key element in qualitative analysis and an important component when striving to ensure trustworthiness in your study [14] . Qualitative research is about seeking out variations and not controlling variables, as in quantitative research. Collaborating with others during analysis lets you tap into multiple perspectives and often makes it easier to see variations in the data, thereby enhancing the quality of your results as well as contributing to the rigor of your study. It is important to note that it is not necessary to force consensus in the findings but one can embrace these variations in interpretation and use that to capture the richness in the data.

Yet there are times when neither openness, pre-understanding, intuition, nor researcher triangulation does the job; for example, when analysing an interview and one is simply confused on how to code certain meaning units. At such times, there are a variety of options. A good starting place is to re-read all the interviews through the lens of this specific issue and actively search for other similar types of meaning units you might have missed. Another way to handle this is to conduct further interviews with specific queries that hopefully shed light on the issue. A third option is to have a follow-up interview with the same person and ask them to explain.

Additional tips

It is important to remember that in a typical project there are several interviews to analyse. Codes found in a single interview serve as a starting point as you then work through the remaining interviews coding all material. Form your categories and themes when all project interviews have been coded.

When submitting an article with your study results, it is a good idea to create a table or figure providing a few key examples of how you progressed from the raw data of meaning units, to condensed meaning units, coding, categorisation, and, if included, themes. Providing such a table or figure supports the rigor of your study [1] and is an element greatly appreciated by reviewers and research consumers.

During the analysis process, it can be advantageous to write down your research aim and questions on a sheet of paper that you keep nearby as you work. Frequently referring to your aim can help you keep focused and on track during analysis. Many find it helpful to colour code their transcriptions and write notes in the margins.

Having access to qualitative analysis software can be greatly helpful in organising and retrieving analysed data. Just remember, a computer does not analyse the data. As Jennings [15] has stated, “… it is ‘peopleware,’ not software, that analyses.” A major drawback is that qualitative analysis software can be prohibitively expensive. One way forward is to use table templates such as we have used in this article. (Three analysis templates, Templates A, B, and C, are provided as supplementary online material ). Additionally, the “find” function in word processing programmes such as Microsoft Word (Redmond, WA USA) facilitates locating key words, e.g., in transcribed interviews, meaning units, and codes.

Lessons learnt/key points

From our experience with content analysis we have learnt a number of important lessons that may be useful for the novice researcher. They are:

• • A method description is a guideline supporting analysis and trustworthiness. Don’t get caught up too rigidly following steps. Reflexivity and flexibility are just as important. Remember that a method description is a tool helping you in the process of making sense of your data by reducing a large amount of text to distil key results.
• • It is important to maintain a vigilant awareness of one’s own pre-understandings in order to avoid bias during analysis and in results.
• • Use and trust your own intuition during the analysis process.
• • If possible, discuss and reflect together with other researchers who have analysed the same data. Be open and receptive to new perspectives.
• • Understand that it is going to take time. Even if you are quite experienced, each set of data is different and all require time to analyse. Don’t expect to have all the data analysis done over a weekend. It may take weeks. You need time to think, reflect and then review your analysis.
• • Keep reminding yourself how excited you have felt about this area of research and how interesting it is. Embrace it with enthusiasm!
• • Let it be chaotic – have faith that some sense will start to surface. Don’t be afraid and think you will never get to the end – you will… eventually!

Peer review under responsibility of African Federation for Emergency Medicine.

Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.afjem.2017.08.001 .

Appendix A. Supplementary data

This paper is in the following e-collection/theme issue:

Published on 16.4.2024 in Vol 26 (2024)

User-Centered Development of a Patient Decision Aid for Choice of Early Abortion Method: Multi-Cycle Mixed Methods Study

Authors of this article:

Original Paper

• Kate J Wahl 1 , MSc   ; 
• Melissa Brooks 2 , MD   ; 
• Logan Trenaman 3 , PhD   ; 
• Kirsten Desjardins-Lorimer 4 , MD   ; 
• Carolyn M Bell 4 , MD   ; 
• Nazgul Chokmorova 4 , MD   ; 
• Romy Segall 2 , BSc, MD   ; 
• Janelle Syring 4 , MD   ; 
• Aleyah Williams 1 , MPH   ; 
• Linda C Li 5 , PhD   ; 
• Wendy V Norman 4, 6 * , MD, MHSc   ; 
• Sarah Munro 1, 3 * , PhD  

1 Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, Canada

2 Department of Obstetrics and Gynecology, Dalhousie University, Halifax, NS, Canada

3 Department of Health Systems and Population Health, School of Public Health, University of Washington, Seattle, WA, United States

4 Department of Family Practice, University of British Columbia, Vancouver, BC, Canada

5 Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada

6 Department of Public Health, Environments and Society, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom

*these authors contributed equally

Corresponding Author:

Kate J Wahl, MSc

Department of Obstetrics and Gynecology

University of British Columbia

4500 Oak Street

Vancouver, BC, V6H 3N1

Phone: 1 4165231923

Email: [email protected]

Background: People seeking abortion in early pregnancy have the choice between medication and procedural options for care. The choice is preference-sensitive—there is no clinically superior option and the choice depends on what matters most to the individual patient. Patient decision aids (PtDAs) are shared decision-making tools that support people in making informed, values-aligned health care choices.

Objective: We aimed to develop and evaluate the usability of a web-based PtDA for the Canadian context, where abortion care is publicly funded and available without legal restriction.

Methods: We used a systematic, user-centered design approach guided by principles of integrated knowledge translation. We first developed a prototype using available evidence for abortion seekers’ decisional needs and the risks, benefits, and consequences of each option. We then refined the prototype through think-aloud interviews with participants at risk of unintended pregnancy (“patient” participants). Interviews were audio-recorded and documented through field notes. Finally, we conducted a web-based survey of patients and health care professionals involved with abortion care, which included the System Usability Scale. We used content analysis to identify usability issues described in the field notes and open-ended survey questions, and descriptive statistics to summarize participant characteristics and close-ended survey responses.

Results: A total of 61 individuals participated in this study. Further, 11 patients participated in think-aloud interviews. Overall, the response to the PtDA was positive; however, the content analysis identified issues related to the design, language, and information about the process and experience of obtaining abortion care. In response, we adapted the PtDA into an interactive website and revised it to include consistent and plain language, additional information (eg, pain experience narratives), and links to additional resources on how to find an abortion health care professional. In total, 25 patients and 25 health care professionals completed the survey. The mean System Usability Scale score met the threshold for good usability among both patient and health care professional participants. Most participants felt that the PtDA was user-friendly (patients: n=25, 100%; health care professionals: n=22, 88%), was not missing information (patients: n=21, 84%; health care professionals: n=18, 72%), and that it was appropriate for patients to complete the PtDA before a consultation (patients: n=23, 92%; health care professionals: n=23, 92%). Open-ended responses focused on improving usability by reducing the length of the PtDA and making the website more mobile-friendly.

Conclusions: We systematically designed the PtDA to address an unmet need to support informed, values-aligned decision-making about the method of abortion. The design process responded to a need identified by potential users and addressed unique sensitivities related to reproductive health decision-making.

Introduction

In total, 1 in 3 pregnancy-capable people in Canada will have an abortion in their lifetimes, and most will seek care early in pregnancy [ 1 ]. Medication abortion (using the gold-standard mifepristone/misoprostol regimen) and procedural abortion are common, safe, and effective options for abortion care in the first trimester [ 2 , 3 ]. The choice between using medications and presenting to a facility for a procedure is a preference-sensitive decision; there is no clinically superior option and the choice depends on what matters most to the individual patient regarding the respective treatments and the features of those options [ 4 - 6 ].

The choice of method of abortion can involve a process of shared decision-making, in which the patient and health care professional share the best available evidence about options, and the patient is supported to consider those options and clarify an informed preference [ 7 ]. There are many types of interventions available to support shared decision-making, including interventions targeting health care professionals (eg, educational materials, meetings, outreach visits, audit and feedback, and reminders) and patients (eg, patient decision aids [PtDA], appointment preparation packages, empowerment sessions, printed materials, and shared decision-making education) [ 8 ]. Of these interventions, PtDAs are well-suited to address challenges to shared decision-making about the method of abortion, including limited patient knowledge, public misinformation about options, poor access to health care professionals with sufficient expertise, and apprehension about abortion counseling [ 9 ].

PtDAs are widely used interventions that support people in making informed, deliberate health care choices by explicitly describing the health problem and decision, providing information about each option, and clarifying patient values [ 10 ]. The results of the 2023 Cochrane systematic review of 209 randomized controlled trials indicate that, compared to usual care (eg, information pamphlets or webpages), the use of PtDAs results in increases in patient knowledge, expectations of benefits and harms, clarity about what matters most to them, and participation in making a decision [ 11 ]. Of the studies included in the systematic review, 1 tested the effect of a PtDA leaflet for method of abortion and found that patients eligible for both medication and procedural abortion who received the PtDA were more knowledgeable, and had lower risk perceptions and decisional conflict than those who were in the control group [ 12 ]. However, that PtDA was developed 20 years ago in the UK health system and was not publicly available. A recent environmental scan of PtDAs for a method of abortion found that other available options meet few of the criteria set by the International Patient Decision Aid Standards (IPDAS) collaboration and do not include language and content optimized for end users [ 9 , 13 ].

Consequently, no PtDAs for method of abortion were available in Canada at the time of this study. This was a critical gap for both patients and health care professionals as, in 2017, mifepristone/misoprostol medication abortion came to the market, offering a new method of choice for people seeking abortion in the first trimester [ 14 ]. Unlike most jurisdictions, in Canada medication abortion is typically prescribed in primary care and dispensed in community pharmacies. Offering a PtDA in preparation for a brief primary care consultation allows the person seeking abortion more time to digest new information, consider their preferences, be ready to discuss their options, and make a quality decision.

In this context, we identified a need for a high-quality and publicly available PtDA to support people in making an informed choice about the method of abortion that reflects what is most important to them. Concurrently, our team was working in collaboration with knowledge users (health care professionals, patients, and health system decision makers) who were part of a larger project to investigate the implementation of mifepristone in Canada [ 15 , 16 ]. We, therefore, aimed to develop and evaluate the usability of a web-based PtDA for the Canadian context, where abortion care is publicly funded and available without legal restriction.

Study Design

We performed a mixed methods user-centered development and evaluation study informed by principles of integrated knowledge translation. Integrated knowledge translation is an approach to collaborative research in which researchers and knowledge users work together to identify a problem, conduct research as equal partners to address that problem, and coproduce research products that aim to impact health service delivery [ 17 ]. We selected this approach to increase the likelihood that our end PtDAs would be relevant, useable, and used for patients and health care professionals in Canada [ 17 ]. The need for a PtDA was identified through engagement with health care professionals. In 2017, they highlighted the need for patients to be supported in choosing between procedural care—which historically represented more than 90% of abortions in Canada [ 18 ]—and the newly available medication option [ 19 , 20 ]. This need was reaffirmed in 2022 by the Canadian federal health agency, Health Canada, which circulated a request for proposals to generate “evidence-based, culturally-relevant information aimed at supporting people in their reproductive decision-making and in accessing abortion services as needed” [ 21 ].

We operationalized integrated knowledge translation principles in a user-centered design process. User-centered design “grounds the characteristics of an innovation in information about the individuals who use that innovation, with a goal of maximizing ‘usability in context’” [ 22 ]. In PtDA development, user-centered design involves iteratively understanding users, developing and refining a prototype, and observing user interaction with the prototype [ 23 , 24 ]. Like integrated knowledge translation, this approach is predicated on the assumption that involving users throughout the process increases the relevance of the PtDA and the likelihood of successful implementation [ 24 ].

Our design process included the following steps ( Figure 1 ): identification of evidence about abortion patients’ decisional needs and the attributes of medication and procedural abortion that matter most from a patient perspective; development of a paper-based prototype; usability testing via think-aloud interviews with potential end users; refinement of the PtDA prototype into an interactive website; usability testing via a survey with potential end users and abortion health care professionals; and final revisions before launching the PtDA for real-world testing. Our systematic process was informed by user-centered methods for PtDA development [ 23 , 24 ], guidance from the IPDAS collaboration [ 25 - 27 ], and the Standards for Universal Reporting of Patient Decision Aid Evaluation checklist [ 10 ].

Our multidisciplinary team included experts in shared decision-making (SM and LT), a PhD student in patient-oriented knowledge translation (KJW), experts in integrated knowledge translation with health care professionals and policy makers (WVN and SM), clinical experts in abortion counseling and care (WVN and MB), a medical undergraduate student (RS), a research project coordinator (AW), and family medicine residents (KD-L, CMB, NC, and JS) who had an interest in abortion care. Additionally, a panel of experts external to the development process reviewed the PtDA for clinical accuracy following each revision of the prototype. These experts included coauthors of the national Society for Obstetricians and Gynaecologists of Canada (SOGC) clinical practice guidelines for abortion care in Canada. They were invited to this project because of their knowledge of first-trimester abortion care as well as their ability to support the implementation of the PtDA in guidelines and routine clinical practice.

Ethical Considerations

The research was approved by the University of British Columbia Children’s and Women’s Research Ethics Board (H16-01006) and the Nova Scotia Health Research Ethics Board (1027637). In each round of testing, participants received a CAD $20 (US $14.75) Amazon gift card by email for their participation.

Preliminary Work: Identification of Evidence

We identified the decisional needs of people seeking early abortion care using a 2018 systematic review of reasons for choosing an abortion method [ 28 ], an additional search that identified 1 study conducted in Canada following the 2017 availability of mifepristone/misoprostol medication abortion [ 29 ], and the SOGC clinical practice guidelines [ 2 , 3 ]. The review identified several key factors that matter most for patient choice of early abortion method: perceived simplicity and “naturalness,” fear of complication or bleeding , fear of anesthesia or surgery , timing of the procedure , and chance of sedation . The additional Canadian study found that the time required to complete the abortion and side effects were important factors. According to the SOGC clinical practice guidelines, the key information that should be communicated to the patient are gestational age limits and the risk of complications with increasing gestational age [ 2 , 3 ]. The guidelines also indicate that wait times , travel times , and cost considerations may be important in a person’s choice of abortion method and should be addressed [ 2 , 3 ].

We compiled a long list of attributes for our expert panel and then consolidated and refined the attribute list through each stage of the prototype evaluation. For evidence of how these factors differed for medication and procedural abortion, we drew primarily from the SOGC clinical practice guidelines for abortion [ 2 , 3 ]. For cost considerations, we described the range of federal, provincial, and population-specific programs that provide free coverage of abortion care for people in Canada.

Step 1: Developing the Prototype

Our goal was to produce an interactive, web-based PtDA that would be widely accessible to people seeking an abortion in Canada by leveraging the widespread use of digital health information, especially among reproductive-aged people [ 30 ]. Our first prototype was based on a previously identified paper-based question-and-answer comparison grid that presented evidence-based information about the medication and procedural options [ 9 , 31 ]. We calculated readability by inputting the plain text of the paper-based prototype into a Simple Measure of Gobbledygook (SMOG) Index calculator [ 32 ].

We made 2 intentional deviations from common practices in PtDA development [ 33 ]. First, we did not include an “opt-out” or “do nothing” option, which would describe the natural course of pregnancy. We chose to exclude this option to ensure clarity for users regarding the decision point; specifically, our decision point of interest was the method of abortion, not the choice to terminate or continue a pregnancy. Second, we characterized attributes of the options as key points rather than positive and negative features to avoid imposing value judgments onto subjective features (eg, having the abortion take place at home may be beneficial for some people but may be a deterrent for others).

Step 2: Usability Testing of the Prototype

We first conducted usability testing involving think-aloud interviews with patient participants to assess the paper-based prototype. Inclusion criteria included people aged 18-49 years assigned-female-at-birth who resided in Canada and could speak and read English. In January 2020, we recruited participants for the first round of think-aloud interviews [ 34 ] via email and poster advertising circulated to (1) a network of parent research advisors who were convened to guide a broader program of research about pregnancy and childbirth in British Columbia, Canada, and (2) a clinic providing surgical abortion care in Nova Scotia, Canada, as well as snowball sampling with participants. We purposively sought to advertise this study with these populations to ensure variation in age, ethnicity, level of education, parity, and abortion experience. Interested individuals reviewed this study information form and provided consent to participate, before scheduling an interview. The interviewer asked participants to think aloud as they navigated the prototype, for example describing what they liked or disliked, missing information, or lack of clarity. The interviewer noted the participant’s feedback on a copy of the prototype during the interview. Finally, the participant responded to questions adapted from the System Usability Scale [ 35 ], a measure designed to collect subjective ratings of a product’s usability, and completed a brief demographic questionnaire. The interviews were conducted via videoconferencing and were audio recorded. We deidentified the qualitative data and assigned each participant a unique identifier. Then, the interviewer listened to the recording and revised their field notes with additional information including relevant quotes.

For the analysis of think-aloud interviews, we used inductive content analysis to describe the usability and acceptability of different elements of the PtDA [ 36 ]. Further, 3 family medicine residents (KD-L, CMB, and NC) under guidance from a senior coauthor (SM) completed open coding to develop a list of initial categories, which we grouped under higher-order headings. We then organized these results in a table to illustrate usability issues (categories), illustrative participant quotes, and modifications to make. We then used the results of interviews to adapt the prototype into a web-based format, which we tested via further think-aloud interviews and a survey with people capable of becoming pregnant and health care professionals involved with abortion care.

Step 3: Usability Testing of the Website

For the web-based format, we used DecideApp PtDA open-source software, which provides a sustainable solution to the problems of low quality and high maintenance costs faced by web-based PtDAs by allowing developers to host, maintain, and update their tools at no cost. This software has been user-tested and can be accessed by phone, tablet, or computer [ 37 , 38 ]. It organizes a PtDA into 6 sections: Introduction, About Me, My Values, My Choice, Review, and Next Steps. In the My Values section, an interactive values clarification exercise allows users to rank and make trade-offs between attributes of the options. The final pages provide an opportunity for users to make a choice, complete a knowledge self-assessment, and consider the next steps to access their chosen method.

From July to August 2020, we recruited patient and health care professional participants using Twitter and the email list of the Canadian Abortion Providers Support platform, respectively. Participants received an email with a link to the PtDA and were redirected to the survey once they had navigated through the PtDA. As above, inclusion criteria included people aged 18-49 years assigned as female-at-birth who resided in Canada. Among health care professionals, we included eligible prescribers who may not have previously engaged in abortion care (family physicians, residents, nurse practitioners, and midwives), and allied health professionals and stakeholders who provide or support abortion care, who practiced in Canada. All participants had to speak and read English.

The survey included 3 sections: usability, implementation, and participant characteristics. The usability section consisted of the System Usability Scale [ 35 ], and purpose-built questions about what participants liked and disliked about the PtDA. The implementation section included open- and close-ended questions about how the PtDA compares to other resources and when it could be implemented in the care pathway. Patient participants also completed the Control Preference Scale, a validated measure used to determine their preferred role in decision-making (active, collaborative, or passive) [ 39 ]. Data on participant characteristics included gender, abortion experience (patient participants), and abortion practice (health care professional participants). We deidentified the qualitative data and assigned each participant a unique identifier. For the analysis of survey data, we characterized close-ended responses using descriptive statistics, and, following the analysis procedures described in Step 2 in the Methods section, used inductive content analysis of open-ended responses to generate categories associated with usability and implementation [ 36 ]. In 2021, we made minor revisions to the website based on the results of usability testing and published the PtDA for use in routine clinical care.

In the following sections, we outline the results of the development process including the results of the think-aloud interviews and survey, as well as the final decision aid prototype.

Our initial prototype, a paper-based question-and-answer comparison grid, presented evidence-based information comparing medication and procedural abortion. The first version of the prototype also included a second medication abortion regimen involving off-label use of methotrexate, however, we removed this option following a review by the clinical expert panel who advised us that there is very infrequent use of this regimen in Canada in comparison to the gold standard medication abortion option, mifepristone. Other changes at this stage involved clarifying the scope of practice (health care professionals other than gynecologists can perform a procedural abortion), abortion practice (gestational age limit and how the medication is taken), the abortion experience (what to expect in terms of bleeding), and risk (removing information about second- and third-trimester abortion). The updated prototype was finalized by a scientist (SM) and trainee (KJW) with expertise in PtDA development. The prototype (see Multimedia Appendix 1 ) was ultimately 4 pages long and described 18 attributes of each option framed as Frequently Asked Questions, including abortion eligibility (How far along in pregnancy can I be?), duration (How long does it take?), and side effects (How much will I bleed?). The SMOG grade level was 8.4.

Participant Characteristics

We included 11 participants in think-aloud interviews between January and July 2020, including 7 recruited through a parent research advisory network and 4 individuals who had recently attended an abortion clinic. The mean interview duration was 36 minutes (SD 6 minutes). The participants ranged in age from 31 to 37 years. All had been pregnant and 8 out of 11 (73%) participants had a personal experience of abortion (4 participants who had recently attended an abortion clinic and 4 participants from the parent research advisory who disclosed their experience during the interview). The characteristics of the sample are reported in Table 1 .

Overall, participants had a positive view of the paper-based, comparison grid PtDA. In total, 1 participant who had recently sought an abortion said, “I think this is great and super helpful. It would’ve been awesome to have had access to this right away … I don’t think there’s really anything missing from here that I was Googling about” (DA010). The only participant who expressed antichoice views indicated that the PtDA would be helpful to someone seeking to terminate a pregnancy (DA001). Another participant said, “[The PtDA] is not biased, it’s not like you’re going to die. It’s a fact, you know the facts and then you decide whether you want it or not. A lot of people feel it’s so shameful and judgmental, but this is very straightforward. I like it.” (DA002). Several participants stated they felt more informed and knowledgeable about the options.

In response to questions adapted from the System Usability Scale, all 11 participants agreed that the PtDA was easy to use, that most people could learn to use it quickly, and that they felt very confident using the prototype, and disagreed that it was awkward to use. In total, 8 (73%) participants agreed with the statement that the components of the PtDA were well-integrated. A majority of participants disagreed with the statements that the website was unnecessarily complex (n=8, 73%), that they would need the support of an expert to use it (n=8, 73%), that it was too inconsistent (n=9, 82%), and that they would need to learn a lot before using it (n=8, 73%). Further, 2 (18%) participants agreed with the statements that the PtDA was unnecessarily complex and that they would need to learn a lot before using it. Furthermore, 1 (9%) participant agreed with the statement that the PtDA was too inconsistent.

Through inductive analysis of think-aloud interviews, we identified 4 key usability categories: design, language, process, and experience.

Participants liked the side-by-side comparison layout, appreciated the summary of key points to remember, and said that overall, the presented information was clear. For example, 1 participant reflected, “I think it’s very clear ... it’s very simplistic, people will understand the left-hand column is for medical abortion and the right-hand column is for surgical.” (DA005) Some participants raised concerns about the aesthetics of the PtDA, difficulties recalling the headers across multiple pages, and the overall length of the PtDA.

Participants sought to clarify language at several points in the PtDA. Common feedback was that the gestational age limit for the medication and the procedure should be clarified. Participants also pointed out inconsistent use of language (eg, doctor and health care professional) and medical jargon.

Several participants were surprised to learn that family doctors could provide abortion care. Others noted that information about the duration—including travel time—and number of appointments for both medication and procedural abortion could be improved. In addition to clarifying the abortion process, several participants suggested including additional information and resources to help identify an abortion health care professional, understand when to seek help for abortion-related complications, and access emotional support. It was also important to participants that financial impacts (eg, hospital parking and menstrual pads) were included for each option.

Participants provided insight into the description of the physical, psychological, and other consequences associated with the abortion medication and procedure. Participants who had both types of abortion care felt that the description of pain that “may be worse than a period” was inaccurate. Other participants indicated that information about perceived and real risks was distressing or felt out of place, such as correcting myths about future fertility or breast cancer. Some participants indicated that patient stories would be valuable saying, for example, “I think what might be nice to help with the decision-making process is reading stories of people’s experiences” (DA006).

Modifications Made

Changes made based on these findings are described in Table 2 . Key user-centered modifications included transitioning to a web-based format with a consistent color scheme, clarifying who the PtDA is for (for typical pregnancies up to 10 weeks), adding information about telemedicine to reflect guidelines for the provision of abortion during pandemics, and developing brief first-person qualitative descriptions of the pain intensity for each option.

Through analysis of the interviews and consultation with our panel of clinical experts, we also identified that, among the 18 initial attributes in our prototype, 7 had the most relative importance to patients in choosing between medication and procedural abortion. These attributes also represented important differences between each option which forced participants to consider the trade-offs they were willing to make. Thus we moved all other potential attributes into an information section (My Options) that supported the user to gain knowledge before clarifying what mattered most to them by considering the differences between options (My Values).

a PtDA: patient decision aid.

b SOGC: Society of Obstetricians and Gynaecologists of Canada.

Description of the PtDA

As shown in Figure 2 , the revised version of the PtDA resulting from our systematic process is an interactive website. Initially, the title was My Body, My Choice ; however, this was changed to avoid association with antivaccine campaigns that co-opted this reproductive rights slogan. The new title, It’s My Choice or C’est Mon Choix , was selected for its easy use in English and French. The PtDA leads the user through 6 sections:

• The Introduction section provides the user with information about the decision and the PtDA, as well as grids comparing positive and negative features of the abortion pill and procedure, including their chance of benefits (eg, effectiveness), harms (eg, complications), and other relevant factors (eg, number of appointments and cost).
• The About Me section asks the user to identify any contraindications to the methods. It then prompts users to consider their privacy needs and gives examples of how this relates to each option (eg, the abortion pill can be explained to others as a miscarriage; procedural care can be completed quickly).
• The My Values section includes a values clarification exercise, in which the user selects and weights (on a 0-100 scale) the relative importance of at least three of 7 decisional attributes: avoiding pain, avoiding bleeding, having the abortion at home, having an experience that feels like a miscarriage, having fewer appointments, less time off for recovery, and having a companion during the abortion.
• The My Choice section highlights 1 option, based on the attribute weights the user assigned in the My Values section. For instance, if a user strongly preferred to avoid bleeding and have fewer appointments, the software would suggest that a procedural abortion would be a better match. For a user who preferred having the abortion at home and having a companion present, the software would suggest that a medication abortion would be a better match. The user selects the option they prefer.
• The Review section asks the user to complete the 4-item SURE (Sure of Myself, Understand Information, Risk-Benefit Ratio, Encouragement) screening test [ 41 ], and advises them to talk with an expert if they answer “no” to any of the questions. This section also includes information phone lines to ensure that users can seek confidential, accurate, and nonjudgmental support.
• Lastly, in the Next Steps section, users see a summary of their choice and the features that matter most to them, instructions for how to save the results, keep the results private, and find an abortion health care professional. Each section of the PtDA includes a “Leave” button in case users need to navigate away from the website quickly.

We calculated readability by inputting the plain text of the web-based PtDA into a SMOG Index calculator [ 32 ], which assessed the reading level of the web-based PtDA as grade 9.2.

To ensure users’ trust in the information as accurate and unbiased we provided a data declaration on the landing page: “the clinical information presented in this decision aid comes from Society of Obstetricians and Gynaecologists best practice guidelines.” On the landing page, we also specify “This website was developed by researchers at the University of British Columbia and Dalhousie University. This tool is not supported or connected to any pharmaceutical company.”

A total of 50 participants, including 25 patients and 25 health care professionals, reviewed the PtDA website and completed the survey between January and March 2021. The majority of patient (n=23, 92%) and health care professional (n=23, 92%) participants identified as cisgender women. Among patient participants, 16% (n=4) reported one or more previous abortions in various clinical settings. More than half (n=16, 64%) of health care professionals offered care in private medical offices, with other locations including sexual health clinics, community health centers, and youth clinics. Many health care professionals were family physicians (n=11, 44%), and other common types were nurse practitioners (n=7, 28%) and midwives (n=3, 12%). The mean proportion of the clinical practice of each health care professional devoted to abortion care was 18% (SD 13%). Most health care professional respondents (n=18, 72%) were involved with the provision of medication, but not procedural, abortion care. The characteristics of patient and health care professional participants are reported in Table 3 .

a In total, 4 participants reported a history of abortion care, representing 6 abortion procedures.

b Not available.

The mean System Usability Score met the threshold for good usability among both patient (mean 85.7, SD 8.6) and health care professional (mean 80, SD 12) participants, although some health care professionals agreed with the statement, “I found the website to be unnecessarily complex,” (see Multimedia Appendix 3 for the full distribution of responses from patient and health care professionals). All 25 patients and 22 out of 25 (88%) health care professional respondents indicated that the user-friendliness of the PtDA was good or the best imaginable. When asked what they liked most about the PtDA, both participant groups described the ease of use, comparison of options, and the explicit values clarification exercise. When asked what they liked least about the PtDA, several health care professionals and some patients pointed out that it was difficult to use on a cell phone. A summary of usability results is presented in Table 4 .

In total, 21 (84%) patients and 18 (72%) health care professionals felt that the PtDA was not missing any information needed to decide about the method of abortion in early pregnancy. While acknowledging that it is “hard to balance being easy to read/understand while including enough accurate clinical information,” several health care professionals and some patients indicated that the PtDA was too long and repetitive. Among the 4 (16%) patient participants who felt information was missing, the most common suggestion was a tool for locating an abortion health care professional. The 7 (28%) health care professionals who felt information was missing primarily made suggestions about the medical information included in the PtDA (eg, listing midwives as health care professionals with abortion care in scope of practice and the appropriateness of gender-inclusive terminology) and the accessibility of information for various language and cultural groups.

a Not available.

Implementation

Participants viewed the PtDA as a positive addition to current resources. Patients with a history of abortion care described looking for the information on the internet and speaking with friends, family members, and health care professionals. Compared with these sources of information, many patients liked the credibility and anonymity of the PtDA, whereas some disliked that it was less personal than a conversation. Further, 18 (72%) health care professional participants said that the PtDA would add to or replace the resources they currently use in practice. Compared with these other resources, health care professionals liked that the PtDA could be explored by patients independently and that it would support them in thinking about the option that was best for them. The disadvantages of the PtDA compared with existing resources were the length—which health care professionals felt would make it difficult to use in a clinical interaction—and the lack of localized information. In total, 23 each (92%) of patient and health care professional participants felt that they would use the PtDA before a consultation.

Principal Results

We designed a web-based, interactive PtDA for the choice of method of abortion in early pregnancy [ 42 ], taking a user-centered approach that involved usability testing with 36 patients and 25 health care professionals. Both patient and health care professional participants indicated that the PtDA had good usability and would be a valuable resource for decision-making. This PtDA fills a critical need to support the autonomy of patients and shared decision-making with their health care professional related to the preference-sensitive choice of method of abortion.

Comparison With Prior Work

A 2017 systematic review and environmental scan found that existing PtDAs for the method of abortion are of suboptimal quality [ 9 ]. Of the 50 PtDAs identified, all but one were created without expertise in decision aid design (eg, abortion services, reproductive health organizations, and consumer health information organizations); however, the development process for this UK-based pamphlet-style PtDA was not reported. The remaining PtDAs were noninteractive websites, smartphone apps, and PDFs that were not tested with users. The authors found that the information about methods of abortion was presented in a disorganized, inconsistent, and unequal way. Subsequent work has found that existing PtDAs emphasize medical (versus social, emotional, and practical) attributes, do not include values clarification, and can be biased to persuade users of a certain method [ 13 ].

To address some of the challenges identified in the literature, we systematically structured and designed elements of the PtDA following newly proposed IPDAS criteria (eg, showing positive and negative features with equal detail) [ 33 ]. We included an explicit values-clarification exercise, which a recent meta-analysis found to decrease decisional conflict and values-incongruent choices [ 43 ].

We based the decision aid on comprehensive and up-to-date scientific evidence related to the effectiveness and safety of medication abortion and procedural abortion; however, less evidence was available for nonmedical attributes. For example, many existing PtDAs incorrectly frame privacy as a “factual advantage” of medication abortion [ 13 ]. To address this, we included privacy in the About Me section as something that means “different things to different people.” Similarly, evidence suggests that patients who do not feel appropriately informed about the pain associated with their method of abortion are less satisfied with their choice [ 44 , 45 ]; and the degree of pain experienced varies across options and among individuals. Following the suggestion of patient participants to include stories and recognizing that evidence for the inclusion of narratives in PtDAs is emerging [ 46 ], we elected to develop brief first-person qualitative descriptions of the pain experience. The inclusion of narratives in PtDAs may be effective in supporting patients to avoid surprise and regret, to minimize affective forecasting errors, and to “visualize” their health condition or treatment experience [ 46 ]. Guided by the narrative immersion model, our goal was to provide a “real-world preview” of the pain experience [ 47 ].

In addition to integrating user perspectives on the optimal tone, content, and format of the PtDA, user testing provided evidence to inform the future implementation of the PtDA. A clear barrier to the completion of the PtDA during the clinical encounter from the health care professional perspective was its length, supporting the finding of a recent rapid realist review, which theorized that health care professionals are less likely to use long or otherwise complex PtDAs that are difficult to integrate into routine practice [ 48 ]. However, 46 out of 50 (92%) participants endorsed the use of the PtDA by the patient alone before the initial consultation, which was aligned with the patient participant’s preference to take an active role in making the final decision about their method of abortion as well as the best practice of early, pre-encounter distribution of PtDAs [ 48 ].

A unique feature of this PtDA was that it resulted from a broader program of integrated knowledge translation designed to support access to medication abortion once mifepristone became available in Canada in 2017. Guided by the principle that including knowledge users in research yields results that are more relevant and useful [ 49 ], we developed the PtDA in response to a knowledge user need, involved health care professional users as partners in our research process, including as coauthors, and integrated feedback from the expert panel. This parallels a theory of PtDA implementation that proposes that early involvement of health care professionals in PtDA development “creates a sense of ownership, increases buy-in, helps to legitimize content, and ensures the PtDA (content and delivery) is consistent with current practice” thereby increasing the likelihood of PtDA integration into routine clinical settings [ 48 ].

Viewed through an integrated knowledge translation lens, our findings point toward future areas of work to support access to abortion in Canada. Several patient participants indicated a need for tools to identify health care professionals who offer abortion care. Some shared that their primary health care professionals did not offer medication abortion despite it being within their scope of practice, and instead referred them to an abortion clinic for methods of counseling and care. We addressed this challenge in the PtDA by including links to available resources, such as confidential phone lines that link patients to health care professionals in their region. On the website we also indicated that patient users could ask their primary care providers whether they provide abortion care; however, we acknowledge that this may place the patient in a vulnerable position if their health care professional is uncomfortable with, or unable to, provide this service for any reason. Future work should investigate opportunities to shorten the pathway to this time-sensitive care, including how to support patients who use the decision aid to act on their informed preference for the method of abortion. This work may involve developing a tool for patients to talk to their primary care provider about prescribing medication abortion.

Strengths and Limitations

Several factors affect the interpretation of our work. Although potential patient users participated in the iterative development process, the patient perspective was not represented in a formal advisory panel in the same way that the health care professional experts were. Participant characteristics collected for the think-aloud interviews demonstrated that our patient sample did not include people with lower education attainment, for whom the grade level and length of the PtDA could present a barrier [ 50 ]. Any transfer of the PtDA to jurisdictions outside Canada must consider how legal, regulatory, and other contextual factors affect the choice of the method of abortion. Since this study was completed, we have explored additional strategies to address these concerns, including additional user testing with people from equity-deserving groups, drop-down menus to adjust the level of detail, further plain language editing, and videos illustrating core content. Since the focus of this study was usability, we did not assess PtDA effectiveness, including impact on knowledge, decisional conflict, choice predisposition and decision, or concordance; however, a randomized controlled trial currently underway will measure the impact of the PtDA on these outcomes in a clinical setting. Finally, our integrated knowledge translation approach added to the robustness of our study by ensuring that health care professionals and patients were equal partners in the research process. One impact of this partnered approach is that our team has received funding support from Health Canada to implement the website on a national scale for people across Canada considering their abortion options [ 51 ].

Conclusions

The PtDA provides people choosing a method of early abortion and their health care professionals with a resource to understand methods of abortion available in the Canadian context and support to make a values-aligned choice. We designed the PtDA using a systematic approach that included both patient and health care professional participants to help ensure its relevance and usability. Our future work will seek to evaluate the implementation of the PtDA in clinical settings, create alternate formats to enhance accessibility, and develop a sustainable update policy. We will also continue to advance access to abortion care in Canada with our broader integrated knowledge translation program of research.

Acknowledgments

The authors thank the participants for contributing their time and expertise to the design of this tool. Family medicine residents CMB, NC, KD-L, and JS were supported by Sue Harris grants, Department of Family Practice, University of British Columbia. KJW was supported by the Vanier Scholar Award (2020-23). SM was supported by a Michael Smith Health Research BC Scholar Award (18270). WVN was supported by a Canadian Institutes of Health Research and Public Health Agency of Canada Chair in Applied Public Health Research (2014-2024, CPP-329455-107837). All grants underwent external peer review for scientific quality. The funders played no role in the design of this study, data collection, analysis, interpretation, or preparation of this paper.

Data Availability

Our ethics approval has specified the primary data is not available.

Authors' Contributions

KJW, SM, and MB conceived of and designed this study. CMB, NC, and KD-L led interview data collection, analysis, and interpretation with input from SM. RS and JS led survey data collection, analysis, and interpretation with input from SM and MB. AW, LCL, and WVN contributed to the synthesis and interpretation of results. KJW, SM, and LT wrote the first draft of this paper, and all authors contributed to this paper’s revisions and approved the final version.

Conflicts of Interest

None declared.

Patient decision aid prototype.

Raw data for pain narratives.

Full distribution of System Usability Scale scores for patients and providers.

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Abbreviations

Edited by T Leung; submitted 07.05.23; peer-reviewed by G Sebastian, R French, B Zikmund-Fisher; comments to author 11.01.24; revised version received 23.02.24; accepted 25.02.24; published 16.04.24.

©Kate J Wahl, Melissa Brooks, Logan Trenaman, Kirsten Desjardins-Lorimer, Carolyn M Bell, Nazgul Chokmorova, Romy Segall, Janelle Syring, Aleyah Williams, Linda C Li, Wendy V Norman, Sarah Munro. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

“Evaluation yes, but not like this”: Case study of a failed policy in the Mexican high school

This article addresses one of the most heated moments in recent education policy in Mexico, framed within the 2013 Educational Reform: The performance evaluation of high school teachers across the country, and the condition that they would potentially lose their teaching position if they refused to take the evaluation or failed it. Unprecedented in its type and scope, the evaluation triggered controversy throughout the country, polarizing the population on its benefits and negative effects. The purpose of this study is to recover, through a retrospective analysis, the experience, perception, feelings, and emotions that the evaluation generated in a group of high school teachers in the state of Morelos, Mexico. A qualitative methodology was used, employing techniques like the collection of newspaper sources, official documents, and television content, as well as conducting interviews with 25 out of 34 teachers selected by the educational authority to be evaluated. Pre- and post-evaluation interviews were conducted and presented. Among the findings, it was noted that teachers were willing to be evaluated, but considered the evaluation process to be disorganized, unfair, and even punitive.

Author Biographies

Luz marina ibarra uribe, universidad autónoma del estado de morelos.

Antropóloga social por la Escuela Nacional de Antropología e Historia (México), realizó estudios del Historia en la Universidad Nacional Autónoma de México y es doctora en educación por la Universidad Autónoma del Estado de Morelos. Es responsable del Cuerpo Académico Consolidado: Estudios Estratégicos Regionales e integrante de la Red Nacional de Investigadores en Educación y Valores (REDUVAL), del Consejo Mexicano de Investigación Educativa (COMIE), de la Red de Investigación en Emociones y Afectos desde las Ciencias Sociales y las Humanidades (RENISCE Internacional), de la Red Internacional de Colaboración en Temas de Innovación, Emprendimiento y Competitividad para el Desarrollo Económico y social (REDIIEMCO), así como de la Red de Investigación Educativa del CBTis No.76. Sus líneas de investigación son: género, valores, educación y políticas educativas. Actualmente es profesora-investigadora, titular C adscrita a la Facultad de Estudios Superiores de Cuautla (FESC) de la Universidad Autónoma del Estado de Morelos (UAEM). Es perfil deseable PRODEP-SEP; miembro del Sistema Nacional de Investigadoras e Investigadores (SNII). Es autora de los libros: Trayectorias escolares de jóvenes bachilleres atravesadas por la pandemia, El acoso laboral en las instituciones de educación superior. Una visión desde el género y, Abuelas, madres y nietas. Escolaridad y participación ciudadana 1930-1990, publicados por la Universidad Autónoma del Estado de Morelos y coordinador de seis libros más. Autora de diversos artículos y capítulos publicados en revistas indexadas y libros especializados en temas educativos y dictaminadora de diversas revistas especializadas sobre temas de ciencias sociales.

César Darío Fonseca Bautista, Centro de Bachillerato Tecnológico industrial y de servicios No. 76

Anthropologist, Master in Educational Research and Doctor of Education from the UAEM. Attached to the DGETI subsystem. Responsible for the Educational Research Network of his campus. Member of the SNI, COMIE, Reduval and Renisce. Coordinator of the books: Teachers, students and graduates of the industrial technological baccalaureate in the common curricular framework, and Collaborative work in upper secondary education, Author of articles published in specialized magazines, on topics related to upper secondary education.

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Environmental Qualitative Research at EPA

Oak Ridge Associated Universities

• Posted 6 days ago
• Application deadline: 2024-04-18

As a team member, you will assist in the provision of data collection, transcription, and qualitative analysis services to support social science research. The social science research conducted at the EPA’s Great Lakes Toxicology and Ecology Division (GLTED) utilizes case study methods that apply both theory-testing and theory-building approaches. Data collection methods document the behavior and decisions of different stakeholder groups through participant observation, document analysis, and social media mining. The research goal is to understand the decision-making process and how context shapes behavior and preferences related to the environment.

The team member will also have the opportunity to learn about and contribute to environmental decision-making for a real-world problem revolving around ecological outcomes and socioeconomic benefits of restoration and redevelopment of an urban river through a study of how actors navigate the process. The team member will work with other members of GLTED’s Sustainable and Healthy Communities team to conduct participatory community research. Responsibilities include but are not limited to attending meetings, preparing meeting and/or interview transcripts, coding and analyzing interview data, and assisting in background research, writing, and dissemination of results.

To apply for this job please visit www.zintellect.com .