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Qualitative Research : Definition

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

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Research methods--quantitative, qualitative, and more: overview.

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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

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The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

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D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
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• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

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• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
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a method of research that produces descriptive (non-numerical) data, such as observations of behavior or personal accounts of experiences. The goal of gathering this qualitative data is to examine how individuals can perceive the world from different vantage points. A variety of techniques are subsumed under qualitative research, including content analyses of narratives, in-depth interviews, focus groups, participant observation, and case studies, often conducted in naturalistic settings.

SAGE Research Methods Videos

What questions does qualitative research ask.

A variety of academics discuss the meaning of qualitative research and content analysis. Both hypothetical and actual research projects are used to illustrate concepts.

What makes a good qualitative researcher?

Professor John Creswell analyzes the characteristics of qualitative research and the qualitative researcher. He explains that good qualitative researchers tend to look at the big picture, notice details, and write a lot. He discusses how these characteristics tie into qualitative research.

This is just one segment in a series about qualitative research. You can find the rest of the series in our SAGE database, Research Methods: 

Videos covering research methods and statistics

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What is qualitative research.

Qualitative research methodologies seek to capture information that often can't be expressed numerically. These methodologies often include some level of interpretation from researchers as they collect information via observation, coded survey or interview responses, and so on. Researchers may use multiple qualitative methods in one study, as well as a theoretical or critical framework to help them interpret their data.

Qualitative research methods can be used to study:

• How are political and social attitudes formed? 
• How do people make decisions?
• What teaching or training methods are most effective?  

Qualitative Research Approaches

Action research.

In this type of study, researchers will actively pursue some kind of intervention, resolve a problem, or affect some kind of change. They will not only analyze the results but will also examine the challenges encountered through the process. 

Ethnography

Ethnographies are an in-depth, holistic type of research used to capture cultural practices, beliefs, traditions, and so on. Here, the researcher observes and interviews members of a culture — an ethnic group, a clique, members of a religion, etc. — and then analyzes their findings. 

Grounded Theory

Researchers will create and test a hypothesis using qualitative data. Often, researchers use grounded theory to understand decision-making, problem-solving, and other types of behavior.

Narrative Research

Researchers use this type of framework to understand different aspects of the human experience and how their subjects assign meaning to their experiences. Researchers use interviews to collect data from a small group of subjects, then discuss those results in the form of a narrative or story.

Phenomenology

This type of research attempts to understand the lived experiences of a group and/or how members of that group find meaning in their experiences. Researchers use interviews, observation, and other qualitative methods to collect data. 

Often used to share novel or unique information, case studies consist of a detailed, in-depth description of a single subject, pilot project, specific events, and so on. 

• Hossain, M.S., Runa, F., & Al Mosabbir, A. (2021). Impact of COVID-19 pandemic on rare diseases: A case study on thalassaemia patients in Bangladesh. Public Health in Practice, 2(100150), 1-3.
• Nožina, M. (2021). The Czech Rhino connection: A case study of Vietnamese wildlife trafficking networks’ operations across central Europe. European Journal on Criminal Policy and Research, 27(2), 265-283.

Focus Groups

Researchers will recruit people to answer questions in small group settings. Focus group members may share similar demographics or be diverse, depending on the researchers' needs. Group members will then be asked a series of questions and have their responses recorded. While these responses may be coded and discussed numerically (e.g., 50% of group members responded negatively to a question), researchers will also use responses to provide context, nuance, and other details. 

• Dichabeng, P., Merat, N., & Markkula, G. (2021). Factors that influence the acceptance of future shared automated vehicles – A focus group study with United Kingdom drivers. Transportation Research: Part F, 82, 121–140.
• Maynard, E., Barton, S., Rivett, K., Maynard, O., & Davies, W. (2021). Because ‘grown-ups don’t always get it right’: Allyship with children in research—From research question to authorship. Qualitative Research in Psychology, 18(4), 518–536.

Observational Study

Researchers will arrange to observe (usually in an unobtrusive way) a set of subjects in specific conditions. For example, researchers might visit a school cafeteria to learn about the food choices students make or set up trail cameras to collect information about animal behavior in the area. 

• He, J. Y., Chan, P. W., Li, Q. S., Li, L., Zhang, L., & Yang, H. L. (2022). Observations of wind and turbulence structures of Super Typhoons Hato and Mangkhut over land from a 356 m high meteorological tower. Atmospheric Research, 265(105910), 1-18.
• Zerovnik Spela, Kos Mitja, & Locatelli Igor. (2022). Initiation of insulin therapy in patients with type 2 diabetes: An observational study. Acta Pharmaceutica, 72(1), 147–157.

Open-Ended Surveys

Unlike quantitative surveys, open-ended surveys require respondents to answer the questions in their own words. 

• Mujcic, A., Blankers, M., Yildirim, D., Boon, B., & Engels, R. (2021). Cancer survivors’ views on digital support for smoking cessation and alcohol moderation: a survey and qualitative study. BMC Public Health, 21(1), 1-13.
• Smith, S. D., Hall, J. P., & Kurth, N. K. (2021). Perspectives on health policy from people with disabilities. Journal of Disability Policy Studies, 32(3), 224–232.

Structured or Semi-Structured Interviews

Researchers will recruit a small number of people who fit pre-determined criteria (e.g., people in a certain profession) and ask each the same set of questions, one-on-one. Semi-structured interviews will include opportunities for the interviewee to provide additional information they weren't asked about by the researcher.

• Gibbs, D., Haven-Tang, C., & Ritchie, C. (2021). Harmless flirtations or co-creation? Exploring flirtatious encounters in hospitable experiences. Tourism & Hospitality Research, 21(4), 473–486.
• Hongying Dai, Ramos, A., Tamrakar, N., Cheney, M., Samson, K., & Grimm, B. (2021). School personnel’s responses to school-based vaping prevention program: A qualitative study. Health Behavior & Policy Review, 8(2), 130–147.
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The word qualitative implies an emphasis on the qualities of entities and on processes and meanings that are not experimentally examined or measured [if measured at all] in terms of quantity, amount, intensity, or frequency. Qualitative researchers stress the socially constructed nature of reality, the intimate relationship between the researcher and what is studied, and the situational constraints that shape inquiry. Such researchers emphasize the value-laden nature of inquiry. They seek answers to questions that stress how social experience is created and given meaning. In contrast, quantitative studies emphasize the measurement and analysis of causal relationships between variables, not processes. Qualitative forms of inquiry are considered by many social and behavioral scientists to be as much a perspective on how to approach investigating a research problem as it is a method.

Denzin, Norman. K. and Yvonna S. Lincoln. “Introduction: The Discipline and Practice of Qualitative Research.” In The Sage Handbook of Qualitative Research . Norman. K. Denzin and Yvonna S. Lincoln, eds. 3 rd edition. (Thousand Oaks, CA: Sage, 2005), p. 10.

Characteristics of Qualitative Research

Below are the three key elements that define a qualitative research study and the applied forms each take in the investigation of a research problem.

• Naturalistic -- refers to studying real-world situations as they unfold naturally; non-manipulative and non-controlling; the researcher is open to whatever emerges [i.e., there is a lack of predetermined constraints on findings].
• Emergent -- acceptance of adapting inquiry as understanding deepens and/or situations change; the researcher avoids rigid designs that eliminate responding to opportunities to pursue new paths of discovery as they emerge.
• Purposeful -- cases for study [e.g., people, organizations, communities, cultures, events, critical incidences] are selected because they are “information rich” and illuminative. That is, they offer useful manifestations of the phenomenon of interest; sampling is aimed at insight about the phenomenon, not empirical generalization derived from a sample and applied to a population.

The Collection of Data

• Data -- observations yield a detailed, "thick description" [in-depth understanding]; interviews capture direct quotations about people’s personal perspectives and lived experiences; often derived from carefully conducted case studies and review of material culture.
• Personal experience and engagement -- researcher has direct contact with and gets close to the people, situation, and phenomenon under investigation; the researcher’s personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon.
• Empathic neutrality -- an empathic stance in working with study respondents seeks vicarious understanding without judgment [neutrality] by showing openness, sensitivity, respect, awareness, and responsiveness; in observation, it means being fully present [mindfulness].
• Dynamic systems -- there is attention to process; assumes change is ongoing, whether the focus is on an individual, an organization, a community, or an entire culture, therefore, the researcher is mindful of and attentive to system and situational dynamics.

The Analysis

• Unique case orientation -- assumes that each case is special and unique; the first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from and depends upon the quality of individual case studies.
• Inductive analysis -- immersion in the details and specifics of the data to discover important patterns, themes, and inter-relationships; begins by exploring, then confirming findings, guided by analytical principles rather than rules.
• Holistic perspective -- the whole phenomenon under study is understood as a complex system that is more than the sum of its parts; the focus is on complex interdependencies and system dynamics that cannot be reduced in any meaningful way to linear, cause and effect relationships and/or a few discrete variables.
• Context sensitive -- places findings in a social, historical, and temporal context; researcher is careful about [even dubious of] the possibility or meaningfulness of generalizations across time and space; emphasizes careful comparative case study analysis and extrapolating patterns for possible transferability and adaptation in new settings.
• Voice, perspective, and reflexivity -- the qualitative methodologist owns and is reflective about her or his own voice and perspective; a credible voice conveys authenticity and trustworthiness; complete objectivity being impossible and pure subjectivity undermining credibility, the researcher's focus reflects a balance between understanding and depicting the world authentically in all its complexity and of being self-analytical, politically aware, and reflexive in consciousness.

Berg, Bruce Lawrence. Qualitative Research Methods for the Social Sciences . 8th edition. Boston, MA: Allyn and Bacon, 2012; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 2nd ed. Thousand Oaks, CA: Sage Publications, 1995; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Basic Research Design for Qualitative Studies

Unlike positivist or experimental research that utilizes a linear and one-directional sequence of design steps, there is considerable variation in how a qualitative research study is organized. In general, qualitative researchers attempt to describe and interpret human behavior based primarily on the words of selected individuals [a.k.a., “informants” or “respondents”] and/or through the interpretation of their material culture or occupied space. There is a reflexive process underpinning every stage of a qualitative study to ensure that researcher biases, presuppositions, and interpretations are clearly evident, thus ensuring that the reader is better able to interpret the overall validity of the research. According to Maxwell (2009), there are five, not necessarily ordered or sequential, components in qualitative research designs. How they are presented depends upon the research philosophy and theoretical framework of the study, the methods chosen, and the general assumptions underpinning the study. Goals Describe the central research problem being addressed but avoid describing any anticipated outcomes. Questions to ask yourself are: Why is your study worth doing? What issues do you want to clarify, and what practices and policies do you want it to influence? Why do you want to conduct this study, and why should the reader care about the results? Conceptual Framework Questions to ask yourself are: What do you think is going on with the issues, settings, or people you plan to study? What theories, beliefs, and prior research findings will guide or inform your research, and what literature, preliminary studies, and personal experiences will you draw upon for understanding the people or issues you are studying? Note to not only report the results of other studies in your review of the literature, but note the methods used as well. If appropriate, describe why earlier studies using quantitative methods were inadequate in addressing the research problem. Research Questions Usually there is a research problem that frames your qualitative study and that influences your decision about what methods to use, but qualitative designs generally lack an accompanying hypothesis or set of assumptions because the findings are emergent and unpredictable. In this context, more specific research questions are generally the result of an interactive design process rather than the starting point for that process. Questions to ask yourself are: What do you specifically want to learn or understand by conducting this study? What do you not know about the things you are studying that you want to learn? What questions will your research attempt to answer, and how are these questions related to one another? Methods Structured approaches to applying a method or methods to your study help to ensure that there is comparability of data across sources and researchers and, thus, they can be useful in answering questions that deal with differences between phenomena and the explanation for these differences [variance questions]. An unstructured approach allows the researcher to focus on the particular phenomena studied. This facilitates an understanding of the processes that led to specific outcomes, trading generalizability and comparability for internal validity and contextual and evaluative understanding. Questions to ask yourself are: What will you actually do in conducting this study? What approaches and techniques will you use to collect and analyze your data, and how do these constitute an integrated strategy? Validity In contrast to quantitative studies where the goal is to design, in advance, “controls” such as formal comparisons, sampling strategies, or statistical manipulations to address anticipated and unanticipated threats to validity, qualitative researchers must attempt to rule out most threats to validity after the research has begun by relying on evidence collected during the research process itself in order to effectively argue that any alternative explanations for a phenomenon are implausible. Questions to ask yourself are: How might your results and conclusions be wrong? What are the plausible alternative interpretations and validity threats to these, and how will you deal with these? How can the data that you have, or that you could potentially collect, support or challenge your ideas about what’s going on? Why should we believe your results? Conclusion Although Maxwell does not mention a conclusion as one of the components of a qualitative research design, you should formally conclude your study. Briefly reiterate the goals of your study and the ways in which your research addressed them. Discuss the benefits of your study and how stakeholders can use your results. Also, note the limitations of your study and, if appropriate, place them in the context of areas in need of further research.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Heath, A. W. The Proposal in Qualitative Research. The Qualitative Report 3 (March 1997); Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 3rd edition. Thousand Oaks, CA: Sage, 1999; Maxwell, Joseph A. "Designing a Qualitative Study." In The SAGE Handbook of Applied Social Research Methods . Leonard Bickman and Debra J. Rog, eds. 2nd ed. (Thousand Oaks, CA: Sage, 2009), p. 214-253; Qualitative Research Methods. Writing@CSU. Colorado State University; Yin, Robert K. Qualitative Research from Start to Finish . 2nd edition. New York: Guilford, 2015.

Strengths of Using Qualitative Methods

The advantage of using qualitative methods is that they generate rich, detailed data that leave the participants' perspectives intact and provide multiple contexts for understanding the phenomenon under study. In this way, qualitative research can be used to vividly demonstrate phenomena or to conduct cross-case comparisons and analysis of individuals or groups.

Among the specific strengths of using qualitative methods to study social science research problems is the ability to:

• Obtain a more realistic view of the lived world that cannot be understood or experienced in numerical data and statistical analysis;
• Provide the researcher with the perspective of the participants of the study through immersion in a culture or situation and as a result of direct interaction with them;
• Allow the researcher to describe existing phenomena and current situations;
• Develop flexible ways to perform data collection, subsequent analysis, and interpretation of collected information;
• Yield results that can be helpful in pioneering new ways of understanding;
• Respond to changes that occur while conducting the study ]e.g., extended fieldwork or observation] and offer the flexibility to shift the focus of the research as a result;
• Provide a holistic view of the phenomena under investigation;
• Respond to local situations, conditions, and needs of participants;
• Interact with the research subjects in their own language and on their own terms; and,
• Create a descriptive capability based on primary and unstructured data.

Anderson, Claire. “Presenting and Evaluating Qualitative Research.” American Journal of Pharmaceutical Education 74 (2010): 1-7; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Limitations of Using Qualitative Methods

It is very much true that most of the limitations you find in using qualitative research techniques also reflect their inherent strengths . For example, small sample sizes help you investigate research problems in a comprehensive and in-depth manner. However, small sample sizes undermine opportunities to draw useful generalizations from, or to make broad policy recommendations based upon, the findings. Additionally, as the primary instrument of investigation, qualitative researchers are often embedded in the cultures and experiences of others. However, cultural embeddedness increases the opportunity for bias generated from conscious or unconscious assumptions about the study setting to enter into how data is gathered, interpreted, and reported.

Some specific limitations associated with using qualitative methods to study research problems in the social sciences include the following:

• Drifting away from the original objectives of the study in response to the changing nature of the context under which the research is conducted;
• Arriving at different conclusions based on the same information depending on the personal characteristics of the researcher;
• Replication of a study is very difficult;
• Research using human subjects increases the chance of ethical dilemmas that undermine the overall validity of the study;
• An inability to investigate causality between different research phenomena;
• Difficulty in explaining differences in the quality and quantity of information obtained from different respondents and arriving at different, non-consistent conclusions;
• Data gathering and analysis is often time consuming and/or expensive;
• Requires a high level of experience from the researcher to obtain the targeted information from the respondent;
• May lack consistency and reliability because the researcher can employ different probing techniques and the respondent can choose to tell some particular stories and ignore others; and,
• Generation of a significant amount of data that cannot be randomized into manageable parts for analysis.

Research Tip

Human Subject Research and Institutional Review Board Approval

Almost every socio-behavioral study requires you to submit your proposed research plan to an Institutional Review Board. The role of the Board is to evaluate your research proposal and determine whether it will be conducted ethically and under the regulations, institutional polices, and Code of Ethics set forth by the university. The purpose of the review is to protect the rights and welfare of individuals participating in your study. The review is intended to ensure equitable selection of respondents, that you have met the requirements for obtaining informed consent , that there is clear assessment and minimization of risks to participants and to the university [read: no lawsuits!], and that privacy and confidentiality are maintained throughout the research process and beyond. Go to the USC IRB website for detailed information and templates of forms you need to submit before you can proceed. If you are  unsure whether your study is subject to IRB review, consult with your professor or academic advisor.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Labaree, Robert V. "Working Successfully with Your Institutional Review Board: Practical Advice for Academic Librarians." College and Research Libraries News 71 (April 2010): 190-193.

Another Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

NOTE :  For a list of online communities, research centers, indispensable learning resources, and personal websites of leading qualitative researchers, GO HERE .

For a list of scholarly journals devoted to the study and application of qualitative research methods, GO HERE .

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Qualitative Research Using R: A Systematic Approach pp 1–19 Cite as

Qualitative Research: An Overview

• Yanto Chandra 3 &
• Liang Shang 4  
• First Online: 24 April 2019

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Qualitative research is one of the most commonly used types of research and methodology in the social sciences. Unfortunately, qualitative research is commonly misunderstood. In this chapter, we describe and explain the misconceptions surrounding qualitative research enterprise, why researchers need to care about when using qualitative research, the characteristics of qualitative research, and review the paradigms in qualitative research.

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Qualitative research is defined as the practice used to study things –– individuals and organizations and their reasons, opinions, and motivations, beliefs in their natural settings. It involves an observer (a researcher) who is located in the field , who transforms the world into a series of representations such as fieldnotes, interviews, conversations, photographs, recordings and memos (Denzin and Lincoln 2011 ). Many researchers employ qualitative research for exploratory purpose while others use it for ‘quasi’ theory testing approach. Qualitative research is a broad umbrella of research methodologies that encompasses grounded theory (Glaser and Strauss 2017 ; Strauss and Corbin 1990 ), case study (Flyvbjerg 2006 ; Yin 2003 ), phenomenology (Sanders 1982 ), discourse analysis (Fairclough 2003 ; Wodak and Meyer 2009 ), ethnography (Geertz 1973 ; Garfinkel 1967 ), and netnography (Kozinets 2002 ), among others. Qualitative research is often synonymous with ‘case study research’ because ‘case study’ primarily uses (but not always) qualitative data.

The quality standards or evaluation criteria of qualitative research comprises: (1) credibility (that a researcher can provide confidence in his/her findings), (2) transferability (that results are more plausible when transported to a highly similar contexts), (3) dependability (that errors have been minimized, proper documentation is provided), and (4) confirmability (that conclusions are internally consistent and supported by data) (see Lincoln and Guba 1985 ).

We classify research into a continuum of theory building — >   theory elaboration — >   theory testing . Theory building is also known as theory exploration. Theory elaboration refers to the use of qualitative data and a method to seek “confirmation” of the relationships among variables or processes or mechanisms of a social reality (Bartunek and Rynes 2015 ).

In the context of qualitative research, theory/ies usually refer(s) to conceptual model(s) or framework(s) that explain the relationships among a set of variables or processes that explain a social phenomenon. Theory or theories could also refer to general ideas or frameworks (e.g., institutional theory, emancipation theory, or identity theory) that are reviewed as background knowledge prior to the commencement of a qualitative research project.

For example, a qualitative research can ask the following question: “How can institutional change succeed in social contexts that are dominated by organized crime?” (Vaccaro and Palazzo 2015 ).

We have witnessed numerous cases in which committed positivist methodologists were asked to review qualitative papers, and they used a survey approach to assess the quality of an interpretivist work. This reviewers’ fallacy is dangerous and hampers the progress of a field of research. Editors must be cognizant of such fallacy and avoid it.

A social enterprises (SE) is an organization that combines social welfare and commercial logics (Doherty et al. 2014 ), or that uses business principles to address social problems (Mair and Marti 2006 ); thus, qualitative research that reports that ‘social impact’ is important for SEs is too descriptive and, arguably, tautological. It is not uncommon to see authors submitting purely descriptive papers to scholarly journals.

Some qualitative researchers have conducted qualitative work using primarily a checklist (ticking the boxes) to show the presence or absence of variables, as if it were a survey-based study. This is utterly inappropriate for a qualitative work. A qualitative work needs to show the richness and depth of qualitative findings. Nevertheless, it is acceptable to use such checklists as supplementary data if a study involves too many informants or variables of interest, or the data is too complex due to its longitudinal nature (e.g., a study that involves 15 cases observed and involving 59 interviews with 33 informants within a 7-year fieldwork used an excel sheet to tabulate the number of events that occurred as supplementary data to the main analysis; see Chandra 2017a , b ).

As mentioned earlier, there are different types of qualitative research. Thus, a qualitative researcher will customize the data collection process to fit the type of research being conducted. For example, for researchers using ethnography, the primary data will be in the form of photos and/or videos and interviews; for those using netnography, the primary data will be internet-based textual data. Interview data is perhaps the most common type of data used across all types of qualitative research designs and is often synonymous with qualitative research.

The purpose of qualitative research is to provide an explanation , not merely a description and certainly not a prediction (which is the realm of quantitative research). However, description is needed to illustrate qualitative data collected, and usually researchers describe their qualitative data by inserting a number of important “informant quotes” in the body of a qualitative research report.

We advise qualitative researchers to adhere to one approach to avoid any epistemological and ontological mismatch that may arise among different camps in qualitative research. For instance, mixing a positivist with a constructivist approach in qualitative research frequently leads to unnecessary criticism and even rejection from journal editors and reviewers; it shows a lack of methodological competence or awareness of one’s epistemological position.

Analytical generalization is not generalization to some defined population that has been sampled, but to a “theory” of the phenomenon being studied, a theory that may have much wider applicability than the particular case studied (Yin 2003 ).

There are different types of contributions. Typically, a researcher is expected to clearly articulate the theoretical contributions for a qualitative work submitted to a scholarly journal. Other types of contributions are practical (or managerial ), common for business/management journals, and policy , common for policy related journals.

There is ongoing debate on whether a template for qualitative research is desirable or necessary, with one camp of scholars (the pluralistic critical realists) that advocates a pluralistic approaches to qualitative research (“qualitative research should not follow a particular template or be prescriptive in its process”) and the other camps are advocating for some form of consensus via the use of particular approaches (e.g., the Eisenhardt or Gioia Approach, etc.). However, as shown in Table 1.1 , even the pluralistic critical realism in itself is a template and advocates an alternative form of consensus through the use of diverse and pluralistic approaches in doing qualitative research.

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Chandra, Y., Shang, L. (2019). Qualitative Research: An Overview. In: Qualitative Research Using R: A Systematic Approach. Springer, Singapore. https://doi.org/10.1007/978-981-13-3170-1_1

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Home Market Research

Qualitative Research Methods: Types, Analysis + Examples

Qualitative research is based on the disciplines of social sciences like psychology, sociology, and anthropology. Therefore, the qualitative research methods allow for in-depth and further probing and questioning of respondents based on their responses. The interviewer/researcher also tries to understand their motivation and feelings. Understanding how your audience makes decisions can help derive conclusions in market research.

What is qualitative research?

Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication .

This method is about “what” people think and “why” they think so. For example, consider a convenience store looking to improve its patronage. A systematic observation concludes that more men are visiting this store. One good method to determine why women were not visiting the store is conducting an in-depth interview method with potential customers.

For example, after successfully interviewing female customers and visiting nearby stores and malls, the researchers selected participants through random sampling . As a result, it was discovered that the store didn’t have enough items for women.

So fewer women were visiting the store, which was understood only by personally interacting with them and understanding why they didn’t visit the store because there were more male products than female ones.

Gather research insights

Types of qualitative research methods with examples

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience with reference to a particular topic. There are different types of qualitative research methods, such as in-depth interviews, focus groups, ethnographic research, content analysis, and case study research that are usually used.

The results of qualitative methods are more descriptive, and the inferences can be drawn quite easily from the obtained data .

Qualitative research methods originated in the social and behavioral research sciences. Today, our world is more complicated, and it is difficult to understand what people think and perceive. Online research methods make it easier to understand that as it is a more communicative and descriptive analysis .

The following are the qualitative research methods that are frequently used. Also, read about qualitative research examples :

1. One-on-one interview

Conducting in-depth interviews is one of the most common qualitative research methods. It is a personal interview that is carried out with one respondent at a time. This is purely a conversational method and invites opportunities to get details in depth from the respondent.

One of the advantages of this method is that it provides a great opportunity to gather precise data about what people believe and their motivations . If the researcher is well experienced, asking the right questions can help him/her collect meaningful data. If they should need more information, the researchers should ask such follow-up questions that will help them collect more information.

These interviews can be performed face-to-face or on the phone and usually can last between half an hour to two hours or even more. When the in-depth interview is conducted face to face, it gives a better opportunity to read the respondents’ body language and match the responses.

2. Focus groups

A focus group is also a commonly used qualitative research method used in data collection. A focus group usually includes a limited number of respondents (6-10) from within your target market.

The main aim of the focus group is to find answers to the “why, ” “what,” and “how” questions. One advantage of focus groups is you don’t necessarily need to interact with the group in person. Nowadays, focus groups can be sent an online survey on various devices, and responses can be collected at the click of a button.

Focus groups are an expensive method as compared to other online qualitative research methods. Typically, they are used to explain complex processes. This method is very useful for market research on new products and testing new concepts.

3. Ethnographic research

Ethnographic research is the most in-depth observational research method that studies people in their naturally occurring environment.

This method requires the researchers to adapt to the target audiences’ environments, which could be anywhere from an organization to a city or any remote location. Here, geographical constraints can be an issue while collecting data.

This research design aims to understand the cultures, challenges, motivations, and settings that occur. Instead of relying on interviews and discussions, you experience the natural settings firsthand.

This type of research method can last from a few days to a few years, as it involves in-depth observation and collecting data on those grounds. It’s a challenging and time-consuming method and solely depends on the researcher’s expertise to analyze, observe, and infer the data.

4. Case study research

T he case study method has evolved over the past few years and developed into a valuable quality research method. As the name suggests, it is used for explaining an organization or an entity.

This type of research method is used within a number of areas like education, social sciences, and similar. This method may look difficult to operate; however , it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in new research. This is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can likely be used in the research.

6. Process of observation

Qualitative Observation is a process of research that uses subjective methodologies to gather systematic information or data. Since the focus on qualitative observation is the research process of using subjective methodologies to gather information or data. Qualitative observation is primarily used to equate quality differences.

Qualitative observation deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing. This doesn’t involve measurements or numbers but instead characteristics.

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative research: data collection and analysis

A. qualitative data collection.

Qualitative data collection allows collecting data that is non-numeric and helps us to explore how decisions are made and provide us with detailed insight. For reaching such conclusions the data that is collected should be holistic, rich, and nuanced and findings to emerge through careful analysis.

• Whatever method a researcher chooses for collecting qualitative data, one aspect is very clear the process will generate a large amount of data. In addition to the variety of methods available, there are also different methods of collecting and recording the data.

For example, if the qualitative data is collected through a focus group or one-to-one discussion, there will be handwritten notes or video recorded tapes. If there are recording they should be transcribed and before the process of data analysis can begin.

• As a rough guide, it can take a seasoned researcher 8-10 hours to transcribe the recordings of an interview, which can generate roughly 20-30 pages of dialogues. Many researchers also like to maintain separate folders to maintain the recording collected from the different focus group. This helps them compartmentalize the data collected.
• In case there are running notes taken, which are also known as field notes, they are helpful in maintaining comments, environmental contexts, environmental analysis , nonverbal cues etc. These filed notes are helpful and can be compared while transcribing audio recorded data. Such notes are usually informal but should be secured in a similar manner as the video recordings or the audio tapes.

B. Qualitative data analysis

Qualitative data analysis such as notes, videos, audio recordings images, and text documents. One of the most used methods for qualitative data analysis is text analysis.

Text analysis is a  data analysis method that is distinctly different from all other qualitative research methods, where researchers analyze the social life of the participants in the research study and decode the words, actions, etc. 

There are images also that are used in this research study and the researchers analyze the context in which the images are used and draw inferences from them. In the last decade, text analysis through what is shared on social media platforms has gained supreme popularity.

Characteristics of qualitative research methods

• Qualitative research methods usually collect data at the sight, where the participants are experiencing issues or research problems . These are real-time data and rarely bring the participants out of the geographic locations to collect information.
• Qualitative researchers typically gather multiple forms of data, such as interviews, observations, and documents, rather than rely on a single data source .
• This type of research method works towards solving complex issues by breaking down into meaningful inferences, that is easily readable and understood by all.
• Since it’s a more communicative method, people can build their trust on the researcher and the information thus obtained is raw and unadulterated.

Qualitative research method case study

Let’s take the example of a bookstore owner who is looking for ways to improve their sales and customer outreach. An online community of members who were loyal patrons of the bookstore were interviewed and related questions were asked and the questions were answered by them.

At the end of the interview, it was realized that most of the books in the stores were suitable for adults and there were not enough options for children or teenagers.

By conducting this qualitative research the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research now the bookstore owner can now keep books for different age categories and can improve his sales and customer outreach.

Such qualitative research method examples can serve as the basis to indulge in further quantitative research , which provides remedies.

When to use qualitative research

Researchers make use of qualitative research techniques when they need to capture accurate, in-depth insights. It is very useful to capture “factual data”. Here are some examples of when to use qualitative research.

• Developing a new product or generating an idea.
• Studying your product/brand or service to strengthen your marketing strategy.
• To understand your strengths and weaknesses.
• Understanding purchase behavior.
• To study the reactions of your audience to marketing campaigns and other communications.
• Exploring market demographics, segments, and customer care groups.
• Gathering perception data of a brand, company, or product.

LEARN ABOUT: Steps in Qualitative Research

Qualitative research methods vs quantitative research methods

The basic differences between qualitative research methods and quantitative research methods are simple and straightforward. They differ in:

• Their analytical objectives
• Types of questions asked
• Types of data collection instruments
• Forms of data they produce
• Degree of flexibility

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Home » Qualitative Research Methods

Qualitative Research Methods

Table of Contents

Definition:

Qualitative Research Methods are a set of techniques and approaches used to collect and analyze data that is non-numerical and subjective in nature. These methods are often used for data collection in social sciences, humanities , and other fields where the focus is on understanding the complexity of human experience, behavior, and culture.

Qualitative Research Methods are as follows:

Interviews involve asking questions of participants to gather information about their experiences, attitudes, beliefs, and opinions.

Role in Qualitative Research

Interviews are a common method of data collection in qualitative research because they allow researchers to gain in-depth understanding of participants’ perspectives and experiences. Interviews can be conducted individually or in groups, and can be structured or unstructured.

An example of using interviews in qualitative research would be conducting in-depth interviews with cancer patients to gain insights into their experiences of receiving treatment and managing their illness.

Focus Groups

Focus groups involve bringing together a small group of participants to discuss a particular topic or issue.

Focus groups are useful for exploring shared experiences and perspectives among a group of people. They can help researchers to gain insights into group dynamics, social norms, and shared beliefs.

An example of using focus groups in qualitative research would be conducting focus groups with parents of children with autism to gain insights into their experiences of navigating the healthcare system and accessing services for their children.

Observations

Observations involve observing participants in their natural setting, either as a participant or non-participant.

Observations are useful for gaining insights into participants’ behaviors, actions, and interactions in their natural environment. They can help researchers to understand the social context in which participants operate.

An example of using observations in qualitative research would be conducting participant observations of students in a classroom to gain insights into their social interactions and learning behaviors.

Document Analysis

Document analysis involves analyzing documents such as texts, images, and videos to gain insights into a particular topic or issue.

Document analysis is useful for exploring historical or cultural contexts, as well as for analyzing policy documents, media coverage, or other sources of information that are relevant to the research question.

An example of using document analysis in qualitative research would be analyzing newspaper articles and editorials to gain insights into public attitudes towards climate change.

Case Studies

Case studies involve conducting in-depth analyses of a single case or a small number of cases.

Case studies are useful for exploring complex or unusual phenomena in depth, and for gaining insights into the social, cultural, or political context in which a particular case is situated.

An example of using case studies in qualitative research would be conducting a case study of a particular community to gain insights into the social, cultural, and economic factors that influence health outcomes.

Ethnography

Ethnography involves conducting fieldwork in a particular cultural or social setting to gain insights into the beliefs, practices, and experiences of the people who live there.

Ethnography is useful for gaining insights into the cultural context in which a particular phenomenon occurs, as well as for exploring the complex interactions between individuals, groups, and institutions in that context.

An example of using ethnography in qualitative research would be conducting fieldwork in a rural community to gain insights into the social, economic, and cultural factors that influence agricultural practices.

Phenomenology

Phenomenology involves exploring the subjective experiences of participants to gain insights into the meaning and significance of those experiences.

Phenomenology is useful for gaining insights into the lived experiences of participants, and for exploring the meaning and significance of those experiences in relation to the research question.

An example of using phenomenology in qualitative research would be exploring the experiences of survivors of domestic violence to gain insights into the emotional and psychological impact of their experiences and the ways in which they have coped with and made sense of their trauma.

Grounded Theory

Grounded theory involves developing a theory or explanation based on the data that emerges from the research process.

Grounded theory is useful for generating new theoretical insights into a particular phenomenon, and for exploring the ways in which different factors interact to produce particular outcomes.

An example of using grounded theory in qualitative research would be exploring the factors that contribute to success in academic settings by collecting data from students, teachers, and administrators and developing a theory that explains the relationships between these factors.

Narrative Inquiry

Narrative inquiry involves collecting and analyzing stories or personal accounts of participants’ experiences.

Narrative inquiry is useful for gaining insights into the ways in which participants make sense of their experiences, and for exploring the cultural, social, and historical contexts in which those experiences occur.

An example of using narrative inquiry in qualitative research would be collecting stories from refugees to gain insights into their experiences of displacement and resettlement, and to explore the ways in which these experiences are shaped by cultural, social, and political factors.

Content Analysis

Content analysis involves analyzing the content of texts, images, or videos to identify themes, patterns, or trends.

Content analysis is useful for exploring how particular messages, themes, or issues are constructed and represented in different contexts, and for identifying patterns or trends in these representations.

An example of using content analysis in qualitative research would be analyzing political speeches to gain insights into the ways in which political leaders construct and communicate their messages to the public.

Visual Methods

Visual methods involve using images or videos to collect data about a particular phenomenon.

Visual methods are useful for exploring the visual and spatial dimensions of a particular phenomenon, and for gaining insights into participants’ experiences and perspectives through visual media.

An example of using visual methods in qualitative research would be using photography to document the experiences of people living in a particular urban neighborhood, and to explore the visual and spatial dimensions of their daily lives.

Art-based Method s

Art-based methods involve using art as a way of collecting data about a particular phenomenon.

Art-based methods are useful for exploring participants’ experiences and perspectives through creative expression, and for gaining insights into the emotional and affective dimensions of their experiences.

An example of using art-based methods in qualitative research would be asking children to draw pictures of their experiences of living in a particular community, and to use these drawings as a way of exploring their emotional and affective responses to their environment.

Also see Qualitative Research

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• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

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Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organisation?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

• Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Article Contents

Introduction, when to use qualitative research, how to judge qualitative research, conclusions, authors' roles, conflict of interest.

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Qualitative research methods: when to use them and how to judge them

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K. Hammarberg, M. Kirkman, S. de Lacey, Qualitative research methods: when to use them and how to judge them, Human Reproduction , Volume 31, Issue 3, March 2016, Pages 498–501, https://doi.org/10.1093/humrep/dev334

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In March 2015, an impressive set of guidelines for best practice on how to incorporate psychosocial care in routine infertility care was published by the ESHRE Psychology and Counselling Guideline Development Group ( ESHRE Psychology and Counselling Guideline Development Group, 2015 ). The authors report that the guidelines are based on a comprehensive review of the literature and we congratulate them on their meticulous compilation of evidence into a clinically useful document. However, when we read the methodology section, we were baffled and disappointed to find that evidence from research using qualitative methods was not included in the formulation of the guidelines. Despite stating that ‘qualitative research has significant value to assess the lived experience of infertility and fertility treatment’, the group excluded this body of evidence because qualitative research is ‘not generally hypothesis-driven and not objective/neutral, as the researcher puts him/herself in the position of the participant to understand how the world is from the person's perspective’.

Qualitative and quantitative research methods are often juxtaposed as representing two different world views. In quantitative circles, qualitative research is commonly viewed with suspicion and considered lightweight because it involves small samples which may not be representative of the broader population, it is seen as not objective, and the results are assessed as biased by the researchers' own experiences or opinions. In qualitative circles, quantitative research can be dismissed as over-simplifying individual experience in the cause of generalisation, failing to acknowledge researcher biases and expectations in research design, and requiring guesswork to understand the human meaning of aggregate data.

As social scientists who investigate psychosocial aspects of human reproduction, we use qualitative and quantitative methods, separately or together, depending on the research question. The crucial part is to know when to use what method.

The peer-review process is a pillar of scientific publishing. One of the important roles of reviewers is to assess the scientific rigour of the studies from which authors draw their conclusions. If rigour is lacking, the paper should not be published. As with research using quantitative methods, research using qualitative methods is home to the good, the bad and the ugly. It is essential that reviewers know the difference. Rejection letters are hard to take but more often than not they are based on legitimate critique. However, from time to time it is obvious that the reviewer has little grasp of what constitutes rigour or quality in qualitative research. The first author (K.H.) recently submitted a paper that reported findings from a qualitative study about fertility-related knowledge and information-seeking behaviour among people of reproductive age. In the rejection letter one of the reviewers (not from Human Reproduction ) lamented, ‘Even for a qualitative study, I would expect that some form of confidence interval and paired t-tables analysis, etc. be used to analyse the significance of results'. This comment reveals the reviewer's inappropriate application to qualitative research of criteria relevant only to quantitative research.

In this commentary, we give illustrative examples of questions most appropriately answered using qualitative methods and provide general advice about how to appraise the scientific rigour of qualitative studies. We hope this will help the journal's reviewers and readers appreciate the legitimate place of qualitative research and ensure we do not throw the baby out with the bath water by excluding or rejecting papers simply because they report the results of qualitative studies.

In psychosocial research, ‘quantitative’ research methods are appropriate when ‘factual’ data are required to answer the research question; when general or probability information is sought on opinions, attitudes, views, beliefs or preferences; when variables can be isolated and defined; when variables can be linked to form hypotheses before data collection; and when the question or problem is known, clear and unambiguous. Quantitative methods can reveal, for example, what percentage of the population supports assisted conception, their distribution by age, marital status, residential area and so on, as well as changes from one survey to the next ( Kovacs et al. , 2012 ); the number of donors and donor siblings located by parents of donor-conceived children ( Freeman et al. , 2009 ); and the relationship between the attitude of donor-conceived people to learning of their donor insemination conception and their family ‘type’ (one or two parents, lesbian or heterosexual parents; Beeson et al. , 2011 ).

In contrast, ‘qualitative’ methods are used to answer questions about experience, meaning and perspective, most often from the standpoint of the participant. These data are usually not amenable to counting or measuring. Qualitative research techniques include ‘small-group discussions’ for investigating beliefs, attitudes and concepts of normative behaviour; ‘semi-structured interviews’, to seek views on a focused topic or, with key informants, for background information or an institutional perspective; ‘in-depth interviews’ to understand a condition, experience, or event from a personal perspective; and ‘analysis of texts and documents’, such as government reports, media articles, websites or diaries, to learn about distributed or private knowledge.

Qualitative methods have been used to reveal, for example, potential problems in implementing a proposed trial of elective single embryo transfer, where small-group discussions enabled staff to explain their own resistance, leading to an amended approach ( Porter and Bhattacharya, 2005 ). Small-group discussions among assisted reproductive technology (ART) counsellors were used to investigate how the welfare principle is interpreted and practised by health professionals who must apply it in ART ( de Lacey et al. , 2015 ). When legislative change meant that gamete donors could seek identifying details of people conceived from their gametes, parents needed advice on how best to tell their children. Small-group discussions were convened to ask adolescents (not known to be donor-conceived) to reflect on how they would prefer to be told ( Kirkman et al. , 2007 ).

When a population cannot be identified, such as anonymous sperm donors from the 1980s, a qualitative approach with wide publicity can reach people who do not usually volunteer for research and reveal (for example) their attitudes to proposed legislation to remove anonymity with retrospective effect ( Hammarberg et al. , 2014 ). When researchers invite people to talk about their reflections on experience, they can sometimes learn more than they set out to discover. In describing their responses to proposed legislative change, participants also talked about people conceived as a result of their donations, demonstrating various constructions and expectations of relationships ( Kirkman et al. , 2014 ).

Interviews with parents in lesbian-parented families generated insight into the diverse meanings of the sperm donor in the creation and life of the family ( Wyverkens et al. , 2014 ). Oral and written interviews also revealed the embarrassment and ambivalence surrounding sperm donors evident in participants in donor-assisted conception ( Kirkman, 2004 ). The way in which parents conceptualise unused embryos and why they discard rather than donate was explored and understood via in-depth interviews, showing how and why the meaning of those embryos changed with parenthood ( de Lacey, 2005 ). In-depth interviews were also used to establish the intricate understanding by embryo donors and recipients of the meaning of embryo donation and the families built as a result ( Goedeke et al. , 2015 ).

It is possible to combine quantitative and qualitative methods, although great care should be taken to ensure that the theory behind each method is compatible and that the methods are being used for appropriate reasons. The two methods can be used sequentially (first a quantitative then a qualitative study or vice versa), where the first approach is used to facilitate the design of the second; they can be used in parallel as different approaches to the same question; or a dominant method may be enriched with a small component of an alternative method (such as qualitative interviews ‘nested’ in a large survey). It is important to note that free text in surveys represents qualitative data but does not constitute qualitative research. Qualitative and quantitative methods may be used together for corroboration (hoping for similar outcomes from both methods), elaboration (using qualitative data to explain or interpret quantitative data, or to demonstrate how the quantitative findings apply in particular cases), complementarity (where the qualitative and quantitative results differ but generate complementary insights) or contradiction (where qualitative and quantitative data lead to different conclusions). Each has its advantages and challenges ( Brannen, 2005 ).

Qualitative research is gaining increased momentum in the clinical setting and carries different criteria for evaluating its rigour or quality. Quantitative studies generally involve the systematic collection of data about a phenomenon, using standardized measures and statistical analysis. In contrast, qualitative studies involve the systematic collection, organization, description and interpretation of textual, verbal or visual data. The particular approach taken determines to a certain extent the criteria used for judging the quality of the report. However, research using qualitative methods can be evaluated ( Dixon-Woods et al. , 2006 ; Young et al. , 2014 ) and there are some generic guidelines for assessing qualitative research ( Kitto et al. , 2008 ).

Although the terms ‘reliability’ and ‘validity’ are contentious among qualitative researchers ( Lincoln and Guba, 1985 ) with some preferring ‘verification’, research integrity and robustness are as important in qualitative studies as they are in other forms of research. It is widely accepted that qualitative research should be ethical, important, intelligibly described, and use appropriate and rigorous methods ( Cohen and Crabtree, 2008 ). In research investigating data that can be counted or measured, replicability is essential. When other kinds of data are gathered in order to answer questions of personal or social meaning, we need to be able to capture real-life experiences, which cannot be identical from one person to the next. Furthermore, meaning is culturally determined and subject to evolutionary change. The way of explaining a phenomenon—such as what it means to use donated gametes—will vary, for example, according to the cultural significance of ‘blood’ or genes, interpretations of marital infidelity and religious constructs of sexual relationships and families. Culture may apply to a country, a community, or other actual or virtual group, and a person may be engaged at various levels of culture. In identifying meaning for members of a particular group, consistency may indeed be found from one research project to another. However, individuals within a cultural group may present different experiences and perceptions or transgress cultural expectations. That does not make them ‘wrong’ or invalidate the research. Rather, it offers insight into diversity and adds a piece to the puzzle to which other researchers also contribute.

In qualitative research the objective stance is obsolete, the researcher is the instrument, and ‘subjects’ become ‘participants’ who may contribute to data interpretation and analysis ( Denzin and Lincoln, 1998 ). Qualitative researchers defend the integrity of their work by different means: trustworthiness, credibility, applicability and consistency are the evaluative criteria ( Leininger, 1994 ).

Trustworthiness

A report of a qualitative study should contain the same robust procedural description as any other study. The purpose of the research, how it was conducted, procedural decisions, and details of data generation and management should be transparent and explicit. A reviewer should be able to follow the progression of events and decisions and understand their logic because there is adequate description, explanation and justification of the methodology and methods ( Kitto et al. , 2008 )

Credibility

Credibility is the criterion for evaluating the truth value or internal validity of qualitative research. A qualitative study is credible when its results, presented with adequate descriptions of context, are recognizable to people who share the experience and those who care for or treat them. As the instrument in qualitative research, the researcher defends its credibility through practices such as reflexivity (reflection on the influence of the researcher on the research), triangulation (where appropriate, answering the research question in several ways, such as through interviews, observation and documentary analysis) and substantial description of the interpretation process; verbatim quotations from the data are supplied to illustrate and support their interpretations ( Sandelowski, 1986 ). Where excerpts of data and interpretations are incongruent, the credibility of the study is in doubt.

Applicability

Applicability, or transferability of the research findings, is the criterion for evaluating external validity. A study is considered to meet the criterion of applicability when its findings can fit into contexts outside the study situation and when clinicians and researchers view the findings as meaningful and applicable in their own experiences.

Larger sample sizes do not produce greater applicability. Depth may be sacrificed to breadth or there may be too much data for adequate analysis. Sample sizes in qualitative research are typically small. The term ‘saturation’ is often used in reference to decisions about sample size in research using qualitative methods. Emerging from grounded theory, where filling theoretical categories is considered essential to the robustness of the developing theory, data saturation has been expanded to describe a situation where data tend towards repetition or where data cease to offer new directions and raise new questions ( Charmaz, 2005 ). However, the legitimacy of saturation as a generic marker of sampling adequacy has been questioned ( O'Reilly and Parker, 2013 ). Caution must be exercised to ensure that a commitment to saturation does not assume an ‘essence’ of an experience in which limited diversity is anticipated; each account is likely to be subtly different and each ‘sample’ will contribute to knowledge without telling the whole story. Increasingly, it is expected that researchers will report the kind of saturation they have applied and their criteria for recognising its achievement; an assessor will need to judge whether the choice is appropriate and consistent with the theoretical context within which the research has been conducted.

Sampling strategies are usually purposive, convenient, theoretical or snowballed. Maximum variation sampling may be used to seek representation of diverse perspectives on the topic. Homogeneous sampling may be used to recruit a group of participants with specified criteria. The threat of bias is irrelevant; participants are recruited and selected specifically because they can illuminate the phenomenon being studied. Rather than being predetermined by statistical power analysis, qualitative study samples are dependent on the nature of the data, the availability of participants and where those data take the investigator. Multiple data collections may also take place to obtain maximum insight into sensitive topics. For instance, the question of how decisions are made for embryo disposition may involve sampling within the patient group as well as from scientists, clinicians, counsellors and clinic administrators.

Consistency

Consistency, or dependability of the results, is the criterion for assessing reliability. This does not mean that the same result would necessarily be found in other contexts but that, given the same data, other researchers would find similar patterns. Researchers often seek maximum variation in the experience of a phenomenon, not only to illuminate it but also to discourage fulfilment of limited researcher expectations (for example, negative cases or instances that do not fit the emerging interpretation or theory should be actively sought and explored). Qualitative researchers sometimes describe the processes by which verification of the theoretical findings by another team member takes place ( Morse and Richards, 2002 ).

Research that uses qualitative methods is not, as it seems sometimes to be represented, the easy option, nor is it a collation of anecdotes. It usually involves a complex theoretical or philosophical framework. Rigorous analysis is conducted without the aid of straightforward mathematical rules. Researchers must demonstrate the validity of their analysis and conclusions, resulting in longer papers and occasional frustration with the word limits of appropriate journals. Nevertheless, we need the different kinds of evidence that is generated by qualitative methods. The experience of health, illness and medical intervention cannot always be counted and measured; researchers need to understand what they mean to individuals and groups. Knowledge gained from qualitative research methods can inform clinical practice, indicate how to support people living with chronic conditions and contribute to community education and awareness about people who are (for example) experiencing infertility or using assisted conception.

Each author drafted a section of the manuscript and the manuscript as a whole was reviewed and revised by all authors in consultation.

No external funding was either sought or obtained for this study.

The authors have no conflicts of interest to declare.

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Structured vs. Unstructured Qualitative Data: Understanding the Differences

Qualitative data analysis is an essential aspect of many research projects. However, the term "qualitative data" can mean different things to different people, depending on their field of study and the methods they use.

Structured data is often used by statisticians and allows for the categorization and ranking of data. Unstructured data comes from sources like interviews and social media and is typically used by researchers. Learn more about both types of data, examples of qualitative data, and their uses below.

Unstructured Data

Subcategories: Textual data, video, audio, images

Examples: Interview transcripts, Observations, literature, social media

Qualitative data can first be defined as any unstructured data that can be collected through interviews, and open-ended questions in surveys, tweets, etc. as well as secondary data, such as journal articles, company reports, and webpages. What all these types of data have in common is that they are unstructured. There are a variety of methods that can be used for content analysis with this type of qualitative data such as thematic analysis, grounded theory, narrative analysis, conversational analysis, to name just a few.

Structured Data

Subcategories: Nominal data, ordinal data

Examples: Gender, hair color, groups, priority status (low, medium, high)

For statisticians, qualitative data is a synonym for categorical data which is structured and can only take a finite number of values – the categories. These categories can be nominal which means there is no inherent order to them, or ordinal, which means that the categories have a natural order. This type of qualitative data is usually analyzed or modeled using methods such as Multiple Correspondence Analysis (MCA) or Supervised Machine Learning tools in classification problems for example.

Two Worlds for a Same Name

Structured and unstructured qualitative data differ in terms of their organization and the methods used to analyze them. While they may be used together to gain a more comprehensive understanding of a phenomenon, it is important for researchers to understand the differences between them to choose the most appropriate methods for collecting and analyzing data.

Content Analysis with Qualitative Data Software

Whether you’re analyzing structured or unstructured data, qualitative data analysis software like NVivo can help streamline the process. With NVivo data analysis, you can upload unstructured data such as interview transcriptions to then autocode for themes and sentiment . Using frequency queries is another time-saver technique that both statisticians and researchers can apply with NVivo as it considers data like gender and demographic.

Plus, with the crosstab query , you can quickly check the spread of coding across cases and demographic variables. For example, you can use the crosstab query to see how often interview respondents refer to a particular topic or issue or compare what different demographic groups have said about a theme.

Listen to our podcast episode Navigating Inductive Content Analysis in Qualitative Research or read the article summary to learn more about content analysis with NVivo qualitative data analysis software (QDA).

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Want to learn more about thematic analysis and grounded theory? Check out these articles!

• Thematic Analysis is More Popular than You Think
• An Overview of Grounded Theory in Qualitative Research

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

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

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

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

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

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

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

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

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

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

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

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

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

Towards a Definition of Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis – What is Qualitative Research?

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

Qualitative and Quantitative

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

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

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

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

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

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

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

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

Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

Grounded Theory

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

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

Defining Qualitative Research

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

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

Distinctions

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

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

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

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

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

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

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

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

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

Improved Understanding

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

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

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

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

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

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

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

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

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

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

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

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

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

Acknowledgements

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

Biographies

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

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

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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• Open access
• Published: 12 April 2024

Healthcare team resilience during COVID-19: a qualitative study

• John W. Ambrose 1 ,
• Ken Catchpole 2 ,
• Heather L. Evans 3 ,
• Lynne S. Nemeth 1 ,
• Diana M. Layne 1 &
• Michelle Nichols 1  

BMC Health Services Research volume  24 , Article number:  459 ( 2024 ) Cite this article

Metrics details

Resilience, in the field of Resilience Engineering, has been identified as the ability to maintain the safety and the performance of healthcare systems and is aligned with the resilience potentials of anticipation, monitoring, adaptation, and learning. In early 2020, the COVID-19 pandemic challenged the resilience of US healthcare systems due to the lack of equipment, supply interruptions, and a shortage of personnel. The purpose of this qualitative research was to describe resilience in the healthcare team during the COVID-19 pandemic with the healthcare team situated as a cognizant, singular source of knowledge and defined by its collective identity, purpose, competence, and actions, versus the resilience of an individual or an organization.

We developed a descriptive model which considered the healthcare team as a unified cognizant entity within a system designed for safe patient care. This model combined elements from the Patient Systems Engineering Initiative for Patient Safety (SEIPS) and the Advanced Team Decision Making (ADTM) models. Using a qualitative descriptive design and guided by our adapted model, we conducted individual interviews with healthcare team members across the United States. Data were analyzed using thematic analysis and extracted codes were organized within the adapted model framework.

Five themes were identified from the interviews with acute care professionals across the US ( N  = 22): teamwork in a pressure cooker , consistent with working in a high stress environment; healthcare team cohesion , applying past lessons to present challenges , congruent with transferring past skills to current situations; knowledge gaps , and altruistic behaviors , aligned with sense of duty and personal responsibility to the team. Participants’ described how their ability to adapt to their environment was negatively impacted by uncertainty, inconsistent communication of information, and emotions of anxiety, fear, frustration, and stress. Cohesion with co-workers, transferability of skills, and altruistic behavior enhanced healthcare team performance.

Working within the extreme unprecedented circumstances of COVID-19 affected the ability of the healthcare team to anticipate and adapt to the rapidly changing environment. Both team cohesion and altruistic behavior promoted resilience. Our research contributes to a growing understanding of the importance of resilience in the healthcare team. And provides a bridge between individual and organizational resilience.

Peer Review reports

Introduction

The COVID-19 pandemic highlighted the complexity and dynamic nature of healthcare systems. It also created a unique opportunity to look at the concept of resilience through the lens of the healthcare team versus the more common approach of situating the concept within the individual or the organization. The early phase of the pandemic was marked by challenges, such as limited access to personal protective equipment, personnel shortages, drug shortages, and increased risks of infection [ 1 , 2 ]. Ensuring patient safety and proper functioning requires coordination and adaptation of the healthcare team and various processes across the health system infrastructure [ 3 , 4 ]. Resilience results from adaptive coordination which enables healthcare systems to maintain routine function in the face of all conditions [ 5 , 6 ].

Resilience in healthcare has been operationalized through resilience engineering, an interdisciplinary aspect of systems engineering focused on promotingpatient safety through the design, implementation, and management of healthcare systems [ 7 , 8 , 9 ] (e.g., how healthcare systems adapt and adjust to maneuver through the daily complexities and challenges to identify effective practices, prevent errors and maintain resilient performance) [ 6 , 8 , 9 , 10 , 11 ]. Resilient performance in healthcare is proposed to be the net result of reaching the threshold of four resilience capabilities within the system: anticipation, the ability to expect and prepare for the unexpected; monitoring, the ability to observe threats to daily system performance; responding, the ability to adapt how the performance is enacted; and learning, the ability to learn from present and past accomplishments within the system [ 12 ]. At present, there is a paucity of research on the resilience of the healthcare team as a cohesive, singular conscious source of knowledge in a highly complex healthcare system. While the resilience of both healthcare systems [ 11 , 13 ] and healthcare workers [ 14 ] has been investigated, there is a gap in knowledge specific to the resilience of the healthcare team as a unified singular consciousness. The circumstances surrounding the COVID-19 pandemic presented a unique opportunity to understand the resilience of the healthcare team in a highly complex system as a singular aware entity within the system; how it acknowledges itself, defines its purpose, and performs under extenuating circumstances. This shifts the emphasis of individual and organization resilience to the resilience in the interconnected healthcare team that extends beyond the boundary of any single person.

The adapted model situates the healthcare team as a cohesive singlular conscious source of knowledge within an intricate and highly complex system [ 15 ]. This model was designed as a bridge between resilience found in individuals within the healthcare system and the organization to emphasize the healthcare team as an aware, unified whole. Our model [ 15 ] combines the existing Systems Engineering Initiative for Patient Safety (SEIPS) model [ 16 ] (version 1), which is based on five domains (organization, person, tasks, technologies, and tools), and environment and the Advanced Team Decision Making Model [ 17 ], which includes components for team performance [ 17 , 18 , 19 ]. Team performance is comprised of team identity, team cognition, team competency, and team metacognition [ 17 , 18 , 19 ]. Team identity describes how the team identifies their purpose to help one another [ 17 ]. Team cognition describes the state of mind of the team, their focus, and common goals [ 17 ]. Team competency describes how well the team accomplishes tasks, and team metacognition describes problem solving and responsibility [ 17 , 19 ], Fig.  1 .

Healthcare Team as a cohesive, singular conscious source of knowledge in a highly complex system. The continuous variegated border represents the singularity and connectedness of the healthcare team within the system. The gears represent the processes, people, technology, and tasks within this highly dynamic healthcare system

The purpose of this qualitative research was to describe resilience in the healthcare team during the COVID-19 pandemic with the healthcare team situated as a singular conscious source of knowledge defined by its collective identity, purpose, competence, and actions. Additionally, we sought to identify factors that may facilitate or hinder the healthcare team from achieving the necessary capabilities to monitor, anticipate, adapt, and learn to meet the standard for resilient performance.

Methodology

A qualitative descriptive design [ 20 , 21 ] was employed. The interview guide was framed using the adapted model to explore various aspects of healthcare team performance (identity, purpose, competence, and cognition). These questions were pilot tested on the first 3 participants and no further changes were needed. Specifically, we aimed to investigate resilience capabilities, decision-making processes, and overall healthcare team performance.

Sampling strategy

A purposive snowball sample was used to identify healthcare team members who worked in U.S. acute care settings between January 2020–December 2020. This sampling method was used to ensure recruitment of participants most likely to have insight into the phenomenon of resilience in the acute care setting.

Inclusion criteria

To explore a wide range of interprofessional experience, participants were recruited across geographic regions and professional roles through personal contacts and social media [ 22 , 23 , 24 , 25 ]. Eligible participants included English-speaking individuals ages 20 and older with a valid personal email address, internet access, and the ability to participate in an online video interview. Potential participants had to be employed full or part-time for any period from January 2020–December 2020 in any of the following acute healthcare environments: emergency room (ER), intensive care unit (ICU), COVID- 19 ICU, COVID-19 floor, gastroenterology inpatient unit, endoscopy suite, operating room (OR), post anesthesia recovery room (PACU), pre-operative holding area, hospital administration, or inpatient medical and/or surgical patient care unit.

Exclusion criteria

Healthcare team members who did not complete the pre-screening survey or failed to schedule an interview were not enrolled.

National recruitment in the U.S

Upon approval by MUSC Institutional Review Board (IRB), registered under Pro00100917, fliers, social media posts on Twitter TM (version 9.34 IOS, San Francisco, California) and Facebook TM (version 390.1 IOS, Menlo Park, CA), and word of mouth were used to initiate recruitment efforts. Interested participants were sent a link to an electronic screening survey explaining the purpose of the study and verifying the respondents’ eligibility to participate. Informed consent was obtained from all subjects.

Data collection

Data were collected via an initial screening questionnaire to determine eligibility. Data were managed using REDCap™ (version 11.2.2) electronic data capture tools hosted at MUSC. Demographic data included age, sex, race, professional role, years of experience, geographic region, patient population served, practice specialty area, and deployment status during the pandemic. Deployment refers to the reassignment of personnel from their primary clinical area to another area to meet the demands of another clinical area without regard for the participant’s clinical expertise. Qualitative data were collected through semi-structured audio video recorded interviews to understand the healthcare team in their natural environment. Recorded interviews were conducted via Microsoft® Teams (version 1.5.00.17261, Microsoft Corporation) from the PIs private office to mitigate the risk of COVID-19 transmission and promote participation across the U.S.

Data monitoring and safety

The quality of the demographic data was monitored to ensure completeness. Potential participants who submitted incomplete responses on the questionnaire were excluded. Interviews were transcribed using software, transcriptions were reviewed and verified for accuracy, and then uploaded to MAXQDA Analytics Pro, Version 2022 (VERBI software) to facilitate data analysis. Transcripts were not returned to the participants. Qualitative codebooks, institutional review board (IRB) logs, and other study records were stored on a secure university server, with access limited to authorized study personnel. Adherence to Consolidated Criteria for Reporting Qualitative Research (COREQ) standards were maintained throughout the study and analysis [ 26 ].

Data analysis

Quantitative analysis.

Demographic data were analyzed using SPSS Statistics for MAC, version 28 (IBM). Both descriptive statistics for the continuous variables of age and years of experience (mean, standard deviation) and frequency tables (age, sex, race, role, geographic region, population served, deployment status) were analyzed.

Qualitative analysis

The Principal Investigator (PI) (JA) and senior mentor (MN) independently coded the interview transcripts. Open coding method was used to identify the categories of data [ 22 , 27 ]. Both a reflexive journal and audit trail were maintained. Codes were identified through induction from participant experiences and verified through weekly consensus meetings, while theoretical deductive analysis was guided by the adapted model and the four resilience capabilities (anticipation, monitoring, responding, learning [ 12 ]. Reflexive thematic analysis (TA) [ 28 , 29 , 30 , 31 ] was used to analyze the coded data and generate themes. Data were collected and categorized into the codebook until no further codes were identified by the PI and research mentor [ 22 , 27 ]. Participant checking was not employed.

Demographics

The eligibility pool was established based on survey completion. Eighty-nine healthcare team members opened the online screening survey; 21 were incomplete and eliminated from the dataset, which left a pool of 68 potential eligible participants. Eligible participants (100%) were contacted by email and phone to determine their interest in completing the study interview. Twenty-two participants completed screening surveys and study interviews between May–September 2021, equating to a 32.5% enrollment rate. Participant interviews lasted between 21 and 91 min with an average of 43 min. None of the interviews were repeated. Participant demographics, including descriptive statistic and role key, are noted in Tables  1 and 2 , respectively.

Five themes were identified: team work in a pressure cooker , healthcare team cohesion , applying past lessons to present challenges , knowledge gaps , and altruistic behaviors .

Teamwork in a pressure cooker

The theme teamwork in a pressure cooker describes the relentless pressures and emotional stressors (e.g., fear, anxiety, frustration, and stress) experienced by the healthcare team from the risks and potential threats associated with COVID-19 contamination and infection. Factors associated with these pressures included risk of COVID-19 exposure, lack of COVID-19 testing, rapid changes to policies and procedures from the standard, personnel shortages, limited physical space, and limited supplies. Exemplary quotes highlighting participant descriptions of these pressures or subthemes are noted in Table  3 .

The healthcare team described an unprecedented level of stress in the workplace as the healthcare team had to adjust to rapidly changing protocols. The lack of protective equipment, shortage of providers to perform patient care and a lack of a familiar clinical routine saturated them in overwhelming pressure and emotions that stuck to them as they navigated uncharted territory. Exemplary quotes highlighting the healthcare team’s descriptions of these emotions are noted in Table  4 .

“It was…uncharted territory for me.” (P1, DIR) “You were stuck in a situation you never— you didn’t know when it was going to end.” (P4, RN PACU) “They have not enough staff—they can’t do it—they—I don’t know what we’re going to do.” (P6, DIR). “When we deployed—trying to get re-accustomed to the changes—with the needs that had to be met was very difficult.” (P10, RN ENDO) “I wasn’t about to sign up for extra time working in under those stressful conditions.” (P17, RN PACU)

The fear of the unknown, combined with the constant need to adapt to rapidly changing circumstances, led to widespread stress, frustration, anxiety, and exhaustion within the healthcare team. This theme was characterized by the constant pressure both inside and outside of work experienced by the healthcare team.

“Driving to the hospital, crying, driving back from the hospital, crying, still doesn’t sum it up— surrounded by people who were just dying. And what could you do?” (P6, DIR) “It was constant. It was terrible. I couldn’t sleep at night. I’d wake up worried.” (P8, ER MD) “It was kind of like just keep sending the Calvary forward—and when one drops, you just walk over them.” (P17, RN PACU) “It was always there—COVID here, COVID there—you never could just completely get away from it. It was basically the center of everybody’s conversation everywhere you went or if you were on the phone with somebody.” (P18, RN COVID ICU) “I was having to call my parents before I’d leave my apartment to go into work— to vent to them and cry— to let out my frustration and my anxiety—and have them essentially convince me to go into work.” (P19, RN ICU). “Working so much— COVID was all that was on my brain—and it was a lot of pressure.” (P22, MGR)

Working during COVID-19 challenged the resilience of the healthcare team in the face of constant fear and uncertainty. The pressure to maintain team performance, while dealing with constant fear associated with the pandemic effected the healthcare team’s resilience.

“I have to tell you that after being in hospital—I don’t feel resilient right now— doing all the things I’ve done—I just want to be out of the hospital— [crying] I can tell you that it will stay with me the rest of my life— It will always stay with me.” (P6, DIR) “I feel like my team has used up all of their resilience. I don’t think there’s much left.” (P8, ER MD)

However, one team member stood out as an exception. They reported the pressures from the environment helped them to make decisions. This demonstrates that environmental pressures affect members of the healthcare team differently. They reported that the pressure and intensity of the situation sharpened their focus and allowed them to make choices more quickly and effectively.

“I make better decisions when I’m under pressure.” (P22, MGR)

Healthcare team cohesion

The theme healthcare team cohesion describes the unique experience of working together during the pandemic that created a means among the healthcare team to form close relationships and unite. This bond was characterized by the emergence of strong interpersonal connections among healthcare professionals during the COVID-19 pandemic. These connections shaped healthcare team relationships and were a factor in the collaborative decision-making processes within healthcare team for their day-to day functions. This cohesive bonding was fueled by the stress and uncertainty of the situation, which brought the healthcare team together illustrated by their solidarity, camaraderie, trust, and empowerment.

“All those decisions, important decisions were made together.” (P7, CRNA) “Everyone felt like they were they were, you know, in a in a battle zone and on the same side—and so that kind of brought people together.” (P8, ER MD) “I think our team worked as one.” (P11, CEO)

Solidarity described the sense of unity evident among the members of the healthcare team. This was characterized by connectedness and a sense of reliance on one another that promoted teamwork and resilience within the team from support both given and received. The sub-theme camaraderie described the close personal connection and support between the healthcare team that went beyond normal social interactions prior to the pandemic. These connections were filled with trust and respect for other healthcare team members.

“I think we were all trying to do the best we could do and help each other do the best they could do—I think early on just camaraderie helped a lot within the department and, you know, just relying on each other for support.” (P8, ER MD) “We knew that we can depend on each other and we all had different skill sets— I think that that was very important—that made us feel secure— rather than going alone.” (P10, RN ENDO) “We [The ICU Nurses] developed a sense of camaraderie that I mean, it’s nothing I’ve ever felt before, like we had to trust each other with our licenses, with our own health—my resiliency came from my coworkers.” (P14, CHG RN) “One of the things that I think the pandemic did in a positive—was—I believe that the teams that I worked for really started to solidify. We leaned on each other. I felt more of a team environment than I had had pre-pandemic—I felt that people were a bit better together. We all needed each other, and we all leaned on each other, and we gave each other support—more so than before COVID- 19.” (P15, CRNA) ”The nurses on the unit were always there for me—they became my friends— my family.” (P19, RN ICU)

The sub theme of empowerment referred to the ability of the healthcare team to confidently make decisions and assume responsibility for their actions within the healthcare setting. This process involved a sense of authority and the ability to exercise agency in decision-making together to respond and adapt to the demands the healthcare team experienced. The combination of solidarity, camaraderie, trust, and empowerment resulted in a strong sense of cohesion within the healthcare team which led to improved relationships and enhanced resilience in their performance.

“I felt that I felt that the team—we all needed each other and we all leaned on each other and we gave each other support—more so than before COVID.” (P15, CRNA) “How do you want to handle this? What’s the plan?—and we collaborated in the true sense of collaboration.” (P15, CRNA) “We just knew that we could count on each other—we knew that we could count on each other at any time if we had questions, because we all worked so closely together during this. We really became a really tight knit group, and it was great.” (P22, MGR)

The benefits of the cohesion found in the healthcare team were significant and apparent during the COVID-19 pandemic. The strengthened relationships and increased resilience allowed for improved communication and collaboration, leading to better patient care and outcomes. Despite these advantages, it was noted by one participant that the relationships developed were not sustained beyond the peak of the pandemic.

“Now that COVID is kind of at bay in our area, it’s kind of gone back to the same way it was— it has not stuck.” (P15, CRNA)

Applying past lessons to present challenges

The theme applying past lessons to present challenges describes how the knowledge and understanding gained from prior participant experiences was used to adapt to the novel clinical and infrastructural challenges faced during the pandemic. Past experiences facilitated the healthcare team to strategize ways to meet the demands of the healthcare system during this time.

Participants described two strategies the healthcare team used to improve the system’s ability to adapt and function effectively: changing roles and deploying personnel. The process of changing roles involved assigning new responsibilities to individuals based on priority-based initiatives, while deployment involved transferring clinical staff from areas with lower patient care needs to those with higher needs to optimize their utilization. Eleven participants (50%) were affected by these strategies. Of these, 73% were assigned to clinical areas for direct patient care, while the remaining 27% underwent a role change to support the operational needs of the system. The participants’ preexisting work relationships, specialized clinical expertise, and leadership abilities helped them adapt to their new clinical and non-clinical roles, which in turn enhanced the resilience of the healthcare team.

“We wanted to make sure that we were putting people into the right area where their skill set could be used the best.” (P1, DIR) “I’m known for moving people forward—I’m also well known for speaking up when I don’t think it is right and there was a lot of stuff that I didn’t think was right— and not only speaking up, I’m also going to come with the solution.” (P6, DIR)

Participants indicated the lessons learned from prior experience positively impacted team performance and improved patient care outcomes. There were two significant examples in the data: the perspective of a nurse who was redeployed to work in an obstetrics unit (P5, ENDO RN) and the perspective of a nursing director (P6, DIR) whose role was changed to develop a program to ensure adequate staffing.

“Because we [the team of interprofessionals] were all very familiar with what we had to do at the task, at handit [the experience of the provision of care] was very fluid—I think it’s because of our years of experience and working with each other for so long that it just worked out very well ”. (P5, ENDO RN) “Staff believed in me when I said I would do something— I could galvanize people because of my reputation of caring for staff, so I was chosen specifically because of my ability to move people forward in spite of things.” (P6, DIR)

Participants identified being assigned to unfamiliar clinical areas or working with unfamiliar patient populations as a barrier that hindered their ability to adapt to clinical situations. The lack of clinical competence among some personnel led to an increase in workload for other healthcare team members, who had to provide additional instruction and guidance on how to complete the task. Decision-makers who deployed nursing staff to a clinical area with higher staffing needs may have believed that the individual nurse had specific clinical skills that would be helpful in that area, and this was not the case.

“She [the patient] felt like it was that he [the new nurse]—really didn’t know what he was doing—not only were we kind of reintroduced to that role of caring for patients where we haven’t been recently, but we’re also in a teaching mode, too, for the new nurses—we had to prioritize how sick the patients were, from basic vital signs to wound dressings to respiratory, and help those new nurses know which to attend to first.” (P10, RN ENDO) “Nurses weren’t really put in a place with enough support and enough resources to be able to do a job, and to do a job that maybe they haven’t done for a few years.” (P10, RN ENDO)

The participants indicated that clinical competencies of a healthcare provider in one patient population may not necessarily be applicable to another patient group. For instance, a neonatal intensive care unit (NICU) nurse who has experience in managing Extra Corporeal Membranous Oxygen (ECMO) in newborns may not have the necessary skills to care for adult ECMO patients in an adult COVID-19 intensive care unit.

“The ECMO nurse was a NICU nurse, so she really could not help me.” (P14, CHG RN)

Knowledge gaps

The theme knowledge gaps refers to the disparity between the existing knowledge of the healthcare team and the knowledge required for the team to effectively respond and adapt to the needs of the healthcare system. The lack of COVID-19 specific knowledge led to gaps in the healthcare team’s understanding, while the lack of communication made it difficult for necessary information to be effectively conveyed and received (e.g., medical logistics, human resources, and other operational policies and procedures). This knowledge gap created a barrier to healthcare team resilience as their capacities to surveil, anticipate, and respond were diminished from the lack of knowledge.

“That [information] is pretty fundamental to how you [the healthcare team] function.” (P17, RN PACU) “I don’t think any amount of preparation could have actually prepared us for how bad COVID was—but we were very, very, very unprepared.” (P18, RN COVID ICU) “It was confusing, it was disruptive to the patients that we had there. They sensed that. And that’s— OK—screw with me, screw with my colleagues, but don’t screw with the patient.” (P21, RN ENDO)

All the participants in leadership roles during the COVID-19 pandemic emphasized the importance of having a thorough understanding of the information and effectively communicating it to the frontline healthcare team members most involved in providing patient care.

“There’s nothing worse than having to learn something in the moment and not being prepared for it.” (P1, DIR) “That made us communicate in multiple ways throughout a day because we all know people learn and adapt it could be in print. It could be in person; it could be a video. We tried to have multiple ways of getting messages out and knowing we needed to repeat messages because this was so unknown, and people were so stressed.” (P11, CEO)

One team member (P13, CRNA), highlighted areas where there were gaps in knowledge in greater detail.

“It was as if the unit was being run by all these sort of substitute teachers that were called in at the last minute. Nobody knew where stuff was—nobody knew what the protocol was—the communication was terrible.” (P13, CRNA)

The cumulative effect from the knowledge gaps contributed to the lack of a practical working knowledge for the healthcare team and affected the healthcare team’s ability to anticipate what needed to be done and adapt their performance to accomplish it. Despite knowledge gaps, healthcare team members reported their capability to learn was facilitated by incremental gains in practical knowledge through their experience over time.

“—people got to be experts at protecting patients and keeping themselves safe.” (P8, ER MD) “I think it kind of was like on the job training at that point, I felt like we were all just trying to survive—learning was like—you went out —then you came back, and you would share how things went.” (P15, CRNA) “You tried to educate yourself so you could be safe.” (P17, RN PACU)

The participant responses received from the leadership (CNO, Directors, and Manager) and front-line personnel (administrative staff, nurses, and physicians) regarding the importance of communication highlighted a difference in perspective. Leadership exhibited a strong commitment toward effective communication and made efforts to ensure all healthcare team members were well informed. On the other hand, the frontline participants indicated instances where communication strategies were not perceived as effective.

“I wasn’t contacted by a manager from the unit or anything to be able to reassure, reassure me that things were being followed through and it should be okay, so that was tough.” (P10, RN ENDO) “It really seemed like there was no communication between—like staffing and the floor—we would get up to the floor and they would say, who are you? What are you doing here? What are we supposed to do with you?” (P20, RN OR)

Altruistic behaviors

The theme altruistic behaviors , encompasses the participants’ perception of their obligation and accountability to their patients and healthcare team, and their steadfastness in supporting the healthcare team even if it meant facing personal or professional repercussions. This readiness to aid the healthcare team and accept consequences showcased their altruism and commitment to the healthcare team. The team’s dedication to both their patients and each other was a primary focus driven by a strong sense of responsibility and obligation.

“I want to be able to look myself in the mirror and feel like I did the right thing—.” (P6, DIR) “My resiliency came from my coworkers. I wanted to come back to work to help them.” (P14, RN COVID ICU) “People really looked out for each other—and people were really kind and compassionate to each other—we all were in this together.” (P15, CRNA) “I’m grateful for the experience that I had and all of the different patients that I was able to help in my time there definitely solidified that being a nurse is what I needed to do—and why I chose the profession is exactly what I should have been doing.” (P19, RN ICU) “You just have to go with what seems right—.” (P22, MGR)

A defining characteristic of this theme was a willingness to endure consequences for the benefit of the healthcare team. These consequences varied from contracting the virus, facing criticism from the healthcare team, to foregoing financial incentives, and even job loss.

“I felt like I was punished for speaking up and I was punished for doing the right thing for patients.” (P6, DIR) “I mean, I literally broke the law so many times. Do you know how many times I started pressors [vasoactive drugs to increase blood pressure] on patients that I had no orders for [because a physician would not enter the ICU]?” (P14, CHG RN)

We identified five key themes based on the coded data; namely teamwork in a pressure cooker , healthcare team cohesion , applying past lessons to present challenges , knowledge gaps , and altruistic behaviors . The researchers propose that stressors arising from the COVID-19 pandemic had an impact on the healthcare team’s resilience. In addition, strong healthcare team cohesion, selfless behaviors among the healthcare team, shared knowledge, and job competence within the healthcare team, enhanced resilient performance.

The healthcare team experienced significant stress and uncertainty, due to the COVID-19 pandemic. This is consistent with previous research that has shown that the unprecedented nature of the pandemic led to challenging working conditions, limited resources, lack of information, and concerns about infecting loved ones [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. The collective global impact of COVID-19 on healthcare systems is likely a contributing factor to these stressors [ 45 , 46 , 47 , 48 ].

Our study, along with those conducted by Anjara et al. (2021)[ 49 ] and Kaye-Kauderer et al. (2022) [ 50 ], found that solidarity and camaraderie among healthcare professionals improve resilience. Specifically, Anjara et al. observed increased collaboration among the healthcare professionals they studied in Ireland during the COVID-19 pandemic, while Kaye-Kauderer et al. identified team camaraderie among their sample of front-line healthcare workers from New York. Kinsella et al. (2023) [ 51 ] reported that COVID − 19 offered frontline workers in the UK the opportunity to work together toward a common goal. Potential explanations for these findings align with the concepts of social capital proposed by Coleman [ 52 ] and social identification with other as proposed by Drury [ 54 ]. Coleman suggests an individual’s skills and capabilities are enhanced through their interdependent relationships with others [ 52 ]. Drury found in communities affected by disasters, mutual aid and support emerged from a shared social identity, which serves to strengthen the community [ 53 ]. Brooks et al. (2021) [ 54 ] conducted a study with healthcare, police, and commercial sectors in England. They found it was important for these individuals to receive support from and provide support to their colleagues to mitigate the psychological impact of disaster exposure [ 54 ]. In addition, like our findings, Aufegger and colleague’s 2019 systematic review [ 55 ] found that social support in acute care healthcare teams creates a supportive atmosphere where team members help each other communicate problems, fulfill needs, and deal with stress.

Our results are consistent with those of Liu et al. (2020) [ 32 ] and Banerjee et al. (2021) [ 44 ] who each found that healthcare professionals frequently feel a sense of personal responsibility to overcome challenges. One potential explanation for this may be the influence of collectivism in their cultures. Similarly, our study suggests the sense of camaraderie among healthcare professionals may also contribute to a sense of responsibility and increased altruistic behavior. However, other studies have highlighted different perspectives on healthcare professionals’ sense of responsibility and duty. Godkin and Markwell’s (2003) [ 56 ] revealed that healthcare professionals’ sense of responsibility during the Severe Acute Respiratory Syndrome (SARS) outbreak was dependent on the protective measures and support offered by the healthcare system where most SARS infected patients were hospitalized. More recently, Gray et al. (2021) reported that nurses’ sense of responsibility stems from their ethical obligations, regardless of potential personal or familial risks [ 57 ].

The altruistic behaviors described by our participants helped maintain the performance of the healthcare team. It is too soon to see the long-term impact from working in this high-pressure environment; however, past research by Liu et al. (2012) [ 58 ] and Wu (2009) [ 59 ] demonstrated that “altruistic-risk acceptance” during the SARS outbreak was shown to decrease depressive symptoms among hospital employees in China.

Our research on resilience has important implications for healthcare organizations and professionals. In order to ready themselves for forthcoming events, healthcare systems must emphasize the significance of shared knowledge and its influence on the healthcare team’s ability to foresee and monitor effectively. This knowledge can help the healthcare organization function as a unified entity, rather than as individuals in separate roles or clusters within the organization to improve healthcare team preparedness. Establishing a cohesive, clinically competent healthcare team benefits the organization and the patients served. Measures to enhance social support, improve communication and ensure clinical competence maintain healthcare team resilience.

There are several limitations to consider when interpreting the results of this study. First, the sample was obtained using purposive snowball sampling, which may have introduced sampling bias and may not accurately represent the larger population of healthcare team members who worked during the COVID-19, as 95% of the sample were white. Second, our study did not have equal representation of all interprofessional team members. It is possible that a more heterogenous sample regarding role, race and gender may have introduced additional codes. Additionally, the PI (JA) worked as a Certified Registered Nurse Anesthesiologist (CRNA) in acute care during the pandemic and personal experience may have introduced confirmation bias. Also, the focus of our research was to fill a gap in the existing knowledge of what is known about healthcare team resilience in pandemic disasters, and help to answer if and how it intersects with individual and organizational resilience. It is possible this novel conceptualization of healthcare team as a cohesive singular conscious source of knowledge did not adequately address this.

Steps to ensure rigor and mitigate any potential shortcomings of qualitative data analysis were the maintenance of a reflexive journal, a willingness of the PI to let go of unsupported ideas and constant verification of codes and themes with the research mentor (MN) for coherence and consistency within the coded data, selected methodology and research questions.

Overall, the extracted themes of teamwork in a pressure cooker; healthcare team cohesion; applying past lessons to present challenges; knowledge gaps; and altruistic behaviors illustrate comparable experiences within the healthcare team. As healthcare professionals and organizations continue to navigate the challenges of the COVID-19 pandemic and other crises, our findings provide valuable insights into how team cohesion, along with altruistic behaviors, may enhance resilience capabilities to create and maintain a unified resilient healthcare team.

Data availability

The data for this study are confidential as required by the IRB approval. To protect the anonymity of the participants, the data are not publicly available. Additional information about the research method, Interview questions, informant data, and the study in general can be requested from the corresponding author, J.A.

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Acknowledgements

The authors want to thank all the interviewed healthcare team participants for their time and sharing their personal stories and for their continued service during the COVID-19 pandemic. We would also like to acknowledge Ayaba Logan, the Research and Education Informationist, Mohan Madisetti, the MUSC College of Nursing Director of Research, the staff of the MUSC Center for Academic Excellence and the reviewers of this journal for their constructive criticism.

This research (software, transcription services, etc.) was solely funded by the Principal Investigator, J.A.

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Conceptualization J.A., K.C., L.N., D.L., H.E., and M.N.; methodology J.A. and M.N.; J.A. led the study, recruited the interviewees, conducted interviews, led the data analysis, and drafted the manuscript. J.A., and M.N. conducted the data analyses; review and editing K.C., H.E., D.L., and M.N.; supervision M.N.; research project administration J.A. and M.N.; funding acquisition J.A. All authors reviewed the manuscript.

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Ambrose, J.W., Catchpole, K., Evans, H.L. et al. Healthcare team resilience during COVID-19: a qualitative study. BMC Health Serv Res 24 , 459 (2024). https://doi.org/10.1186/s12913-024-10895-3

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Pregnant and postpartum women’s experiences of the indirect impacts of the COVID-19 pandemic in high-income countries: a qualitative evidence synthesis

• Annie Tan 1 , 2 , 3 ,
• Amanda Blair 2 , 4 ,
• Caroline SE. Homer 1 , 2 ,
• Robin Digby 1 , 3 , 5 ,
• Joshua P. Vogel 1 , 2 &
• Tracey Bucknall 1 , 3 , 5  

BMC Pregnancy and Childbirth volume  24 , Article number:  262 ( 2024 ) Cite this article

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Pregnant and postpartum women’s experiences of the COVID-19 pandemic, as well as the emotional and psychosocial impact of COVID-19 on perinatal health, has been well-documented across high-income countries. Increased anxiety and fear, isolation, as well as a disrupted pregnancy and postnatal period are widely described in many studies. The aim of this study was to explore, describe and synthesise studies that addressed the experiences of pregnant and postpartum women in high-income countries during the first two years of the pandemic.

A qualitative evidence synthesis of studies relating to women’s experiences in high-income countries during the pandemic were included. Two reviewers extracted the data using a thematic synthesis approach and NVivo 20 software. The GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative research) was used to assess confidence in review findings.

Sixty-eight studies were eligible and subjected to a sampling framework to ensure data richness. In total, 36 sampled studies contributed to the development of themes, sub-themes and review findings. There were six over-arching themes: (1) dealing with public health restrictions; (2) navigating changing health policies; (3) adapting to alternative ways of receiving social support; (4) dealing with impacts on their own mental health; (5) managing the new and changing information; and (6) being resilient and optimistic. Seventeen review findings were developed under these themes with high to moderate confidence according to the GRADE-CERQual assessment.

Conclusions

The findings from this synthesis offer different strategies for practice and policy makers to better support women, babies and their families in future emergency responses. These strategies include optimising care delivery, enhancing communication, and supporting social and mental wellbeing.

Peer Review reports

As of February 2024 SARS-CoV-2 has infected over 774 million people, and 7 million deaths have been attributed to coronavirus 19 (COVID-19) infection [ 1 ]. Maternal and newborn health services are essential for pregnant and postpartum women, and the COVID-19 pandemic significantly altered provision and access to routine care. Reduced services, limited face-to-face care, transition to virtual and remote care, and limited access to maternity care providers were commonly cited as barriers to accessing quality care by pregnant and postpartum women [ 2 , 3 , 4 , 5 , 6 ]. Additionally, reduced lengths of stay within hospitals and restrictions on support people imposed by health facilities have impacted women receiving care and placed an additional burden on nursing and midwifery staff [ 7 , 8 , 9 ]. This had significant impacts on pregnant and postpartum women’s emotional and psychosocial wellbeing.

Pregnant women and their babies were at an increased risk of adverse effects if she contracted SARS-CoV-2 [ 10 , 11 ]. The direct impacts of the COVID-19 pandemic were largely focused on the clinical manifestations of SARS-CoV-2 such as symptoms, risk factors, management and treatment, as well as adverse maternal and newborn outcomes [ 12 , 13 , 14 , 15 ]. However, at a wider level, the impacts of policy changes, health system reforms and changes to maternity care services indirectly affected the provision of care for all women giving birth over this time period. Women’s experiences of the transition from pregnancy to motherhood were also impacted. For example, in many countries, pregnant women were encouraged to homestay at home, receive care through telehealth rather than face-to-face and reduce face-to-face education [ 16 , 17 ]. Isolation from family, friends and peers has negatively impacted women’s mental health, with increased levels of anxiety, depression and stress globally [ 18 , 19 , 20 , 21 ].

Since the beginning of the pandemic, there has been a plethora of qualitative studies on women’s experiences [ 19 , 22 , 23 , 24 , 25 , 26 ] – the significant volume of papers highlights the need for a clear synthesis. Reviews of qualitative evidence have reported pregnant women’s experiences of social support [ 27 ], as well as highlighting the challenges they faced as they embraced motherhood during the pandemic [ 28 ]. Collating the evidence in a systematic and transparent manner will allow policymakers to consider the indirect implications of public health restrictions on the physical, emotional, and psychosocial health and wellbeing of pregnant and postpartum women.

Qualitative evidence synthesis (QES) is an approach that can systematically collate qualitative data in a transparent manner to inform policy and practice [ 29 ]. Findings from a QES can enable a richer interpretation of a particular phenomenon and enable a greater understanding of individual experiences, views and beliefs [ 30 ]. This QES aimed to explore, describe and synthesise the experiences of pregnant and postpartum women living in high-income countries during the first two years of the COVID-19 pandemic. This research method allows a deeper understanding of their views and experiences during this time. It also facilitates identification of areas of improvement for maternity care services, to ensure high-quality care is available at all times.

A QES was undertaken to identify, evaluate and summarise findings from qualitative studies providing a cohesive and transparent documentation of the contextual variations, stakeholder preferences and experiences to ultimately influence policy and practice [ 31 , 32 ]. This type of synthesis integrates diverse perspectives, which is needed to capture the complexity of the indirect impacts of the COVID-19 pandemic on pregnant and postpartum women’s experiences. This QES was structured to include findings from qualitative studies, as well as qualitative findings from mixed-methods studies. Emphasis was placed on including different types of qualitative evidence that can potentially enrich a synthesis, such as narrative data from qualitative components of mixed-methods studies or free-text from questionnaires [ 29 ].

We followed the relevant Cochrane guidelines [ 29 ] and used the “Enhancing transparency in reporting the synthesis of qualitative research” (ENTREQ) statement to guide our approach and reporting (Supplementary 1 , S1) [ 33 ]. In addition, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting the different phases of identifying studies was used as recommended by the ENTREQ statement (S 2 ) [ 34 ]. The protocol and systematic review were not registered.

Eligibility criteria

We defined “indirect impacts of the pandemic on women”, to mean the impact of regulations, recommendations and public health measures enforced by governments as a response to the COVID-19 pandemic had on pregnant and postpartum women and their newborns. We adopted the World Health Organization’s definition of the postpartum period beginning immediately after birth of the baby and extending to six weeks (42 days) after birth [ 35 ].

Participants within these studies were those who were pregnant or within the postpartum period, of childbearing age (15-49 years), and received any type of maternity care during the COVID-19 pandemic. Studies of women with pre-existing comorbidities were also eligible, as well as those focused on migrants, refugee populations or ethnic minority groups. To facilitate exploration of findings from women of diverse backgrounds we have used the term ‘culturally and linguistically diverse (CALD) populations’. We focussed on women living in high-income countries (HICs). Studies were included if they were conducted in countries listed in the Organisation for Economic Co-operation and Development (OECD) [ 36 ] and Human Development Index (HDI) list of “Very high human development” list [ 37 ]. This allowed for similar contexts and countries to be compared.

Eligible study designs were those that addressed the indirect impact of COVID-19 using qualitative methodologies, including phenomenology, ethnography, grounded theory studies and case studies. We also included any study that obtained data through qualitative methods for data collection such as, interviews, focus groups, online forums and document analysis.

The decision to limit the eligibility based on year of publication, to only include studies published in the first two years of the COVID-19 pandemic (1 st Jan 2020 – 1 st Jan 2022) was to emphasise the impact of the stricter restrictions and lockdowns during this time period. Globally, public health measures to reduce spread and transmission included, mandatory quarantine, limiting movement, lockdowns, closure of schools and workplaces and shielding of vulnerable populations. These measures were significantly harsher during the first two years and subsequently relaxed as vaccine roll-outs occurred and infection rates began to decline [ 38 , 39 ]. The Oxford Coronavirus Government Response Tracker reported a stringency index which reiterates the trend of harsher restrictions implemented by governments throughout 2020-2022 time period and reflects the gradual decline after this date [ 40 ].

Search strategy

Six electronic databases (EBSCO Medline, Embase, APA PsycInfo, CINAHL and Maternity and Infant Care (MIDIRS)) were searched to identify all qualitative research articles published between 1 st January 2020 – 1 st January 2022. Search strategy included terms such as, “pregnan*”, “postpartum”, “mother”, “views”, “experiences”, “opinion*”, “indirect”, “COVID-19”, “coronavirus”. The search strategy was reviewed by a university librarian (S 3 ). Search hits from each of the databases were imported into Endnote 20 which was then used as our reference library. References were imported into Covidence for screening [ 41 ].

Study selection and sampling framework

Two review authors (AT, AB) independently screened titles, abstracts and full texts for inclusion, with any conflicts resolved by discussion or consulting a third author. Reasons for exclusion are described within PRISMA flowchart (Fig. 1 ). Sixty-eight studies were included following full-text review. The Cochrane guidelines for QES highlight that for reviews with large amounts of primary studies (50 or more) can result in a high volume of data, which can threaten quality of the synthesis. In such situations, a sampling framework can enhance the quality and diversity of the papers and ensure the number of studies and amount of data are manageable [ 42 , 43 , 44 ]. A QES worked example by Ames et al., 2019 was used as a guide to develop the sampling framework for data richness [ 45 ]. Two independent reviewers scored included studies from 1 to 5 based on the criteria outlined in Table 1 , to ensure that the sampling framework was reliable and replicable. Any conflicts were resolved by discussion, or a third review author was consulted. Studies with a score ≥4 were included for data extraction and are referred to as ‘sampled’ studies (S 4 ).

Reporting of adapted PRISMA flowchart of included and sampled studies in accordance with PRISMA and ENTREQ guidelines [ 33 ]

Quality assessment

The Critically Appraisal Skills Program (CASP) tool for qualitative research was used by two independent review authors (AT, AB) to assess methodological limitations of sampled studies (S 5 ) [ 46 ]. Any disagreements were resolved through discussion, or when required, a third review author was consulted. Sampled studies were graded as no or very minor, minor, moderate or severe concerns with methodological limitations.

Data extraction and synthesis

A “Characteristics of sampled studies” table was created in Excel and details are reported in Table 2 . Two independent reviewers familiarised themselves with the sampled studies and extracted key themes using Braun and Clarke’s reflexive approach to inductive and deductive thematic analysis [ 47 ]. Data were managed using NVivo 20 [ 48 ]. This was an iterative process as many of the themes and sub-themes overlapped and were relevant in many aspects throughout the perinatal period (Table 3 ). The findings were developed iteratively, and periodically shared with the broader team to evolve our interpretation. Any quotes taken from studies were selected as they reiterated findings, and provided additional depth and meaning to review findings.

Extracted data were populated into two tables for analysis. The first table collated quotes and author interpretations of findings (S 6 ), whilst the second table summarised these into review findings (Table 4 ).

Assessment of confidence in the review findings (GRADE-CERQual)

The GRADE-CERQual tool assesses the confidence in review findings from qualitative evidence syntheses [ 83 ]. Lewin et al., 2018 state that that “the approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation” [ 83 ]. The GRADE-CERQual Interactive Summary of Qualitative Findings (iSoQ) online platform was used to manage and assess confidence in review findings [ 84 ].

Confidence in review findings was determined based on four criteria: methodological limitations, coherence, adequacy and relevance [ 83 ]. For each criterion, review authors determined if there were no or very minor, minor, moderate or serious concerns. An overall GRADE-CERQual assessment of confidence was placed on the findings, levels included: high, moderate, low and very low confidence. Review findings are considered at the highest confidence level and downgraded as there are greater concerns for each individual criterion (Table 4 ). This process was conducted by two authors, with any disagreements resolved through discussion and consulting other authors.

Managing our own reflexivity

Throughout the conceptualisation, data collection and analytical process, the authors considered their own individual views and beliefs about maternity care during the COVID-19 pandemic. As clinicians and researchers working on maternity care (including during the pandemic), we recognised that the COVID-19 period impacted indirectly on women and babies, including their experiences of care, their own anxieties and worries. We are public health professionals with diverse backgrounds including nursing and midwifery, maternal and newborn health, epidemiology and qualitative health research. We met regularly, both to explore the findings and the processes but also to ensure that we separated our individual experiences and beliefs on the interpretation of the analysis and the findings. Employing a systematic and transparent approach to the analytical process, such as including reflection notes after analysing each sampled paper, facilitated collaborative discussions, ensure objectivity and reduced the impact of personal biases.

A total of 36 studies contributed to the synthesis of qualitative evidence to understand pregnant and postpartum women’s experiences during the first two years of the pandemic. There were six over-arching themes: (1) dealing with public health restrictions; (2) navigating changing health policies; (3) adapting to alternative ways of receiving social support; (4) dealing with impacts on their own mental health; (5) managing the new and changing information; and (6) being resilient and optimistic. Seventeen sub-themes were developed within these 6 themes and illustrative quotes are presented in Table 3 to demonstrate theme development. Themes were categorised to differentiate major disruptors to the pregnancy and postpartum period and sub-themes aimed to categorise the indirect impacts that occurred within the major themes.

Characteristics of contributing studies

After applying the sampling framework (data richness score ≥4), 36 sampled studies were included for data extraction and analysis. Thirteen out of 36 studies had a score of 5 [ 49 , 52 , 57 , 58 , 61 , 62 , 63 , 66 , 68 , 69 , 71 , 77 , 82 ], with the remainder scoring 4 [ 9 , 16 , 50 , 51 , 53 , 54 , 55 , 56 , 59 , 60 , 64 , 65 , 67 , 70 , 72 , 73 , 74 , 75 , 76 , 78 , 79 , 80 , 81 , 82 ] (S 5 ). Most studies ( N =27/36, 75%) used specific qualitative methodologies, six were mixed-methods studies, two were cross-sectional studies, and one was a case series report.

Studies were conducted across nine countries, almost one-third ( N =10/36, 28%) of studies published from the UK, Canada ( N =7) and the USA ( N =7) (Table 2 ). Country-specific responses to the pandemic largely included border closures, mandatory lockdowns and restrictions on movement; it is interesting to note that Sweden did not mandate this but instead enforced social distancing practices [ 68 ]. Additionally, some studies reported on a specific sub-population of pregnant and postpartum women, for example women from ethnic minority groups, those with pre-existing comorbidities, and those who were COVID-19 positive. Some studies also included results from women with babies who were greater than 6 months of age, and any findings directly from these participants were omitted from analysis where possible.

The number of participants in studies that conducted interviews ranged from 3 to 84, and studies using qualitative data from open-ended questions or survey data included responses from 16 to 4,611 participants. Where demographic data were available, approximately 1,192 women were primiparous (having their first baby) and approximately 8,017 women were surveyed or interviewed postpartum. Sampled studies were generally of high quality and assessment of methodological limitations indicated that 29 studies were assigned “no or very minor concerns”, six studies were assigned “minor concerns”, and one study was assigned “moderate concerns”. When available, quotes obtained from studies have included additional demographic data. Factors included pregnant or postpartum status at time of data collection, parity and geographical location.

Theme 1: Dealing with public health restrictions

The rapid introduction of public health restrictions has had adverse effects on mental health, social isolation, and the pregnancy experience. Women had to navigate these restrictions and adapt accordingly, realising quickly that their pregnancy and postpartum experience was going to be very different from their expectations.

Sub-theme 1.1. Limited support networks from health care system and providers (High confidence)

Support networks were limited. Women felt that they were “on their own”, “unimportant or irrelevant” or treated as “second class citizens” after birth, because of a lack of physical supports from healthcare providers [ 51 , 60 , 61 , 62 , 70 , 72 , 74 ]. Limited or no access to physical and social support networks was commonly cited as a reason for deteriorating mental health.

Sub-theme 1.2. Balancing exposure risk and need for healthy behaviours (High confidence)

Women balanced COVID-19 exposure risks by shielding, either because of health providers recommendations [ 16 , 69 ] or because they felt it was needed to protect their baby [ 50 , 68 , 71 , 77 , 81 ]. Women delayed or postponed antenatal appointments [ 50 , 57 , 69 , 72 , 82 ], opted for induction of labour [ 74 ], or waited until labour was quite advanced before attending hospital [ 60 , 61 , 77 ]. These decisions were due to pandemic-induced fear, and the perceived risk of infection in a high-risk environment such as the hospital [ 16 , 56 , 80 ].

Sub-theme 1.3: Missing out on social opportunities (High confidence)

Women felt sad, unseen and heartbroken that they were not able to have social opportunities, especially sharing their newborns with family and friends [ 9 , 54 , 56 , 61 , 66 , 70 , 71 , 75 , 76 ]. On postnatal wards, women with older children were disappointed that their nuclear families could not visit and bond with their newborn in the early postpartum period [ 56 , 59 , 80 , 82 ]. While this was disappointing for many, one woman described still feeling well-supported, “ we were supposed to have a baby shower, the weekend after everything shut down … definitely got a lot of gifts in the mail and people who drop things off …. [we] feel like even though he’s being born in this super crazy time and he doesn’t necessarily get to meet people in person, that they are excited about him and want to support us” (USA) [ 52 ]. Primiparous women felt that they missed the opportunity to share many “firsts” with extended families - one woman said, “ this is my family’s first grandchild so it just breaks my heart they will miss her whole babyhood” (postpartum, Canada) [ 64 ].

Sub-theme 1.4 : Breastfeeding challenges and triumphs (High confidence)

Women that struggled with the lack of support around breastfeeding said, " when it came time for breastfeeding, I had no idea what to do or any challenges that could come. There were so, so, so many questions and I felt so confused during everything” (postpartum, primiparous, UK) [ 60 ]. Lactation consultations through virtual remote care was considered inadequate by most women [ 51 , 66 , 71 , 73 , 75 ], especially when practical hands-on education and assistance was needed [ 51 , 53 , 72 , 77 ]. These challenges led some women to cease breastfeeding early [ 51 , 62 , 73 ].

Conversely, public health restrictions enforcing women to stay at home allowed some women to practice responsive breastfeeding, without concern for social obligations or visitors [ 51 , 62 , 64 , 71 , 75 , 79 ]. Some women valued this flexibility - “ there’s no right or wrong way. You know, at the end of the day the ultimate goal is that my baby needs to be fed.… you know, feed him breast milk, breast milk, or formula. He’s fed. He’s happy. Sweet. That’s done. Job done! The important thing is actually [to] be kind to yourself, you know?” (postpartum, primiparous, UK) [ 62 ].

Challenges and triumphs were felt by both multiparous and primiparous women [ 51 ]. The difference between experienced and first-time mothers was stark in some studies, highlighted by multiparous women who felt ‘knowledgeable’ and ‘had the experience’, and sharing empathetic messages towards primiparous women with limited breastfeeding support [ 62 , 78 ].However, the lack of face-to-face breastfeeding support meant that first-time mothers and experienced mothers also faced hardships. As one mother recounts her sadness: “ I had virtual appointments [with lactation consultants], which I found totally useless… I was devastated that it wasn’t working with [the new baby] because it was something I was really looking forward to” (postpartum, multiparous, Canada)  [ 73 ].

Theme 2: Navigating changing health policies

The ever-changing nature of the pandemic created periods of uncertainty. Women and their families were expected to accept and adapt to changing health policies which directly impacted their antenatal, labour and birth and postnatal experiences.

Sub-theme 2.1: A birthing experience filled with uncertainty and unknowns (High confidence)

Many women reported that, given the constantly changing policies, they were unsure what to expect for their labour and birth [ 9 , 49 , 60 , 75 , 77 ]. Limitations included not being able to have a water birth, use a bath or the shower, access nitrous oxide gas during labour [ 49 , 74 , 82 ] and others could not have their desired support people present [ 60 , 77 ]. In some cases, women opted for medicalised interventions to retain a sense of control - choosing a caesarean birth to ensure their partner was present at birth [ 60 , 74 ]. Women struggled with the prospect of early discharge, lacking confidence and fearing reduced support at home, with some feeling pushed out of the hospital [ 49 , 53 , 60 , 74 ]. Some women chose to leave hospital early due to the lack of support or poor experiences while in hospital [ 60 ]. Conversely, some women welcomed early discharge, wanting to be away from the hospital and to be reunited with family members [ 62 , 80 ]. Women who tested positive to COVID-19 early in the pandemic described additional challenges, such as a lack of certainty on how care was going to be managed [ 77 ]. They felt this restricted their autonomy over their labour and birth choices.

Sub-theme 2.2: Reduced support and partner presence healthcare settings (High confidence)

Due to the public health restrictions in hospitals, women often missed having their partner and family supports [ 16 , 49 , 57 , 66 , 71 ]. For example, “ one of my coping mechanisms is having my partner there to hear the same things I am hearing because I kind of shut down sometimes when I get too upset. It’s always good to have that second person listening… and walking out with strength of unity ” (pregnant, primiparous, Australia) [ 49 ]. The inability for some women to have their partners present negatively impacted women’s birthing experience [ 53 , 70 , 79 , 80 ], confidence on the postnatal ward and many expressed the sense of being “ robbed of this experience ” (pregnant, UK) [ 75 ].

Sub-theme 2.3: Transitioning to telehealth, virtual and remote care (Moderate confidence)

Public health restrictions limited face-to-face health care appointments with a maternity care provider [ 54 ]. Negative telehealth experiences were expressed predominantly by first-time mothers [ 71 ], with many saying, “ over the phone just doesn’t do it… you don’t get to look into somebody’s eyes and to trust them and for them to say, you’re okay ” (postpartum, Ireland) , adding to their anxieties. This was felt similarly by CALD women as there was a disconnect with health care providers using virtual methods and this was exacerbated for women who were not able to access interpreters [ 80 ]. Positive encounters with telehealth were associated with the increased accessibility to health services and generally preferred by multiparous women [ 54 , 65 , 68 ]. Whilst many were glad that telehealth services were available, this woman highlighted the inequities, “ I think I would question the accessibility of that. Not everyone has a smartphone and expecting people to be able to receive a video call is not necessarily the most inclusive thing ” (postpartum, primiparous, UK) [ 77 ] indicating that some women may have fallen through the gaps of maternity care.

Sub-theme 2.4: Barriers to accessing health services (High confidence)

The closure of so-called non-essential services, such as, physiotherapists, chiropractors, pools and gyms indirectly impacted women [ 66 , 74 ]. This often increased women’s anxiety, stress, feelings of helplessness and frustration [ 16 , 54 , 60 , 74 ] and incidence of postnatal depression [ 82 ]. This also limited opportunities to receive reassurance from healthcare providers, reducing women’s confidence [ 49 , 71 , 72 , 77 ]. Typically, women accessed networks for information and support, such as, family and friends with midwifery clinical expertise, or referred to recent pregnancy experience [ 52 , 68 , 75 , 79 ]. Women had to advocate strongly for physical assessments for themselves and their newborns [ 74 ].

Additionally, women from CALD populations were challenged in accessing culturally appropriate care with changes to interpretation services, “it creates like a…a gap in communication where if something you express is not clearly understood so maybe they could be left with some misinterpretation” (UK) [ 63 ]. Another example of the inequities faced by CALD women was expressed by this woman who did not receive interpretation services during appointments, “ sometimes they explained things to me by using signs and I understand a little English but it’s hard to understand medical terms and they didn’t use an interpreter for this ” (postpartum, multiparous, Canada) [ 80 ].

Theme 3: Adapting to alternative ways of receiving social support

Support networks, such as, family and friends, peer support groups (e.g. mother’s groups), and formal support from maternity care providers provide the foundation for a healthy and positive pregnancy and postpartum period. The COVID-19 pandemic forced women to adapt and seek support in different ways.

Sub-theme 3.1: Accessing support through different avenues (Moderate confidence)

Support from family and friends was accessed in different ways, for example, utilising video call technologies to be able to see faces helped with the grief of not being able to be present [ 16 ]. Women who were able to establish pregnancy and mother’s groups during the pandemic were grateful that they had these supports. Alternatively, women created or sought support through online social media platforms [ 61 , 68 , 70 , 81 ], to share a sense of camaraderie that they were not alone in their experiences [ 52 , 77 ]. In these forums, women shared information about COVID-19 developments, updates to hospital policies, and utilised others as sounding boards for advice. Some women reported greater support from partners who had transitioned to working from home [ 51 , 62 , 64 , 66 , 75 ]. Although virtual technologies allowed women to bridge the gap of social distancing, they wanted the physical connection with others.

Sub-theme 3.2: Desiring connection with family and friends (High confidence)

Women felt they needed intergenerational support to raise their newborns, and this was especially important during difficult times. Many had planned for parents to come and support them [ 81 ], as they believed that, “ the older generation have more experience on what babies need or what they feel… with my other two [children]… they knew exactly what would make them feel better ” (pregnant, multigravida, Australia) [ 49 ]. Some women struggled without the additional support, the lack of sleep impeded their physical wellbeing [ 61 , 73 , 75 ], and the isolation from family impacted their mental health [ 9 , 49 , 56 , 60 , 61 , 73 ]. In some cases, women were able to “ quarantine with family ”, providing women with a “ strong support network ” (postpartum, Canada) as they transitioned into motherhood [ 59 ]. Gradually, as public health restrictions eased, women from the UK felt government responses did not consider new mothers and babies and they called for “social bubbles” for families to receive the additional support [ 62 , 72 ]. The loss of informal support networks was apparent for some CALD women. As this woman said, “it was really hard during COVID. In Syria I had my family… but to give birth here with no one with me?! I needed someone with me, my neighbours, my friends… I felt like I was drowning” (postpartum, multiparous, Canada) [ 80 ].

Theme 4: Dealing with impacts on their own mental health

The COVID-19 pandemic placed a significant toll on pregnant and postpartum women’s mental health at all stages of the pandemic. Public health strategies failed to include protective measures for mental health, as such many women reported increased levels of fear, anxiety, stress, loneliness and depression.

Sub-theme 4.1: Managing anxiety due to virus-related fears and concerns (Moderate confidence)

Women often experienced anxiety exacerbated by the pandemic, for example, “ as a new mom you are already so nervous, so adding a pandemic to that pile of anxiety and worry ” (postpartum, Canada) [ 70 ]. This was related to possibility of infection, particularly in hospital and healthcare settings [ 9 , 56 , 57 , 69 , 82 ], and the need to protect their unborn or newborn baby [ 50 , 72 , 80 ]. Some faced additional challenges as migrants from another country, “ I found it very hard when you’re coming to the country without knowing anyone and the coronavirus, lockdown was very difficult, I was very depressed. I was very anxious… I feel worried a lot ” (UK) [ 63 ].

Sub-theme 4.2: Feeling lonely and isolated (High confidence)

Loneliness and isolation were commonly reported as women faced motherhood alone without their usual support systems. One woman said, “ it was quite sad that I couldn’t even share my pregnancy experience with anyone, and I feel like I missed out ” (postpartum, Australia) [ 54 ]. Feelings of loneliness was especially felt by mothers who were not able to have their partners present during birth or postnatally [ 61 ]. Women were not able to build supportive peer networks in their antenatal and postnatal periods [ 49 , 62 , 73 , 74 , 75 , 78 , 81 ], with one woman saying, “ there’s nothing like just meeting people or, just naturally building friendships when you go to baby groups” (postpartum, multiparous, UK)  [ 62 ] emphasising the importance of developing social relationships. Cancellation of appointments and lack of face-to-face care added to feelings of “ abandonment ” and “ being forgotten ” [ 9 , 60 , 62 , 70 , 72 , 73 ].

Theme 5: Managing new and changing information

Due to the novelty of COVID-19 and lack of information about adverse effects, maternity care services had to rapidly adapt as new data came to light. Women described the need to search, access and filter useful information, a process which was challenging for many.

Sub-theme 5.1: Constantly changing advice and information (High confidence)

The constantly changing advice was distressing [ 82 ]. These changes meant a lot of uncertainty, one woman said, “ at 34 weeks I had a telephone appointment and I tried to ask what the changes in hospitals were, because of COVID and talk about the birth plan. She basically said, ‘everything is changing so quickly there is no point in us even talking about that now. Wait until your next appointment’ ” (postpartum, primiparous, UK) [ 77 ]. This limited women’s ability to adequately plan and prepare for the birth. Some women described following the updates from government officials and hospitals overwhelming [ 66 ]. As restrictions eased, women described the frustrations they had with the slow adaptations by health services, “ when I got to the hospital, they didn’t know about the restrictions having been lifted … That was really frustrating because I was like why? Why does this hospital not know?” (Australia) [ 82 ] and the differences between health services, “ restrictions have still not been lifted in ‘Hospital A’ whereas they have been eased in both ‘Hospital B’ and ‘Hospital C’ ” (pregnant, multiparous, Ireland) [ 9 ].

Sub-theme 5.2: Inadequate information from healthcare providers (Moderate confidence)

Women felt there was not enough information from healthcare providers, “ I think there was a lot of confusion; there was no good communication about what was happening to appointments. You weren’t really sure; were they happening on the phone [telehealth], when were you going to get the call? There was very little communication. So, I always felt a bit uneasy about that… ” (postpartum, primiparous, UK) [ 77 ]. Some information was contradictory [ 60 ] for example, “ I’ve found the disconnect between the information that my GP was getting and that the [hospital] was getting – they weren’t getting the same ” (Australia) [ 82 ]. Women wanted clear information that was easily accessed by the lay person [ 9 , 16 , 54 , 61 , 65 , 66 , 67 , 68 , 75 ]. They also wanted uncertainty to be acknowledged, “ it would have been useful to have some generic information that went out to women in that situation… statements from a medical professional to put people’s minds at ease ” (postpartum, Australia) [ 54 ].

Theme 6: Being resilient and optimistic

Many women were self-reliant and took it upon themselves to remain positive and proactive throughout the perinatal period.

Sub-theme 6.1: Self-help strategies to overcome challenges of the pandemic (High confidence)

Women developed their own strategies to find solace and support [ 77 ]. When asked what advice they had for other women in similar situations, advocacy for oneself was frequently reported [ 66 , 67 , 70 , 71 , 77 , 79 , 81 , 82 ]. In contrast, another woman regretted not voicing her concerns, “ I have naively trusted that the hospital gives me the information I need … Then I realized afterwards that there were many moms who were much angrier than me and said much more; insisted much more… and I simply did not; I regret it a bit ” (postpartum, Norway) [ 67 ]. Women reported coping using different strategies, such as being outdoors and active [ 16 , 52 , 54 ], limiting news and access to social media platforms [ 54 , 69 , 70 , 81 ], seeking professional help [ 58 , 73 ], informing themselves about the virus [ 58 , 71 ], drawing on their own faith and religion [ 52 , 69 ] and self-reassurance [ 50 , 52 , 62 ]. Many complied with public health restrictions, however there were some women that decided their mental health and physical wellbeing was more of a priority and broke public health restrictions to seek support from family and friends [ 62 , 66 , 73 ]. Despite the challenges faced during the pandemic, some women reported high resilience, positive childbirth and postnatal experiences, and feeling empowered by their ability to overcome challenging circumstances [ 54 , 58 , 74 ].

Sub-theme 6.2: Making the most out of the positive encounters (Moderate confidence)

The lack of visitors on the postnatal ward and in homes was described by women as “ pleasant ”, “ relaxing ” and a “ blessing in disguise ” as women were able to recover and establish undisrupted routines with their newborns [ 54 , 71 , 72 ]. A commonly reported positive outcome of limiting social obligations was the ability to establish successful breastfeeding, one woman said, “ I was inundated with visitors with my first child and often could not feed responsively… With my second child, there is none of that pressure and I can really see an enormous difference both is his feeding and in my mental health ” (postpartum, UK) [ 51 ]. Women also described health services as “ peaceful ”, as there were fewer people in waiting rooms, appointments were quick, social distancing was enforced and use of PPE limited the possibility of transmission [ 16 , 49 , 71 , 75 , 81 , 82 ].

Sub-theme 6.3: Information seeking and desire for more information (Moderate confidence)

Women obtained information from official government documents, guidelines released by professional bodies, the news, social media and platforms run by professional academics [ 53 , 66 , 68 , 72 , 81 ]. Reasons to seek information included: to clarify any uncertainties about risk and infection, keep up to date with COVID-19 guidelines and to be informed about changes to hospital policies [ 49 , 52 , 66 , 69 , 77 ]. Even once women were provided with information, poor communication and follow up left women feeling dissatisfied [ 54 ]. One woman shared advice about engaging with different information sources – “ you can’t just trust them – you’ve got to decipher through what’s true and what’s not… Is that actually having a positive influence on me, and my mental and physical health, or not? And if it’s a no, well why am I engaging in this ?” (Australia) [ 81 ].

This QES synthesised data from 36 sampled studies on pregnant and postpartum women’s experiences from high income countries during the COVID-19 pandemic. Findings were categorised under six overarching themes and 17 review findings to understand their experiences as the pandemic unfolded. Women had to navigate the transition from pregnancy to motherhood, whilst also adapting to the complexities of the COVID-19 pandemic. High to moderate confidence was placed in these review findings, indicating the strength of the evidence.

This review highlights that pregnant and postnatal women across high-income countries faced similar yet inherently unique experiences and challenges. During the pandemic, primiparous women faced moderate-to-high prenatal stress levels, as they recounted their first pregnancy experience during a time of significant uncertainty [ 85 , 86 , 87 ]. On the other hand, some evidence highlighted that multiparous women were ‘adaptive’ and felt ‘prepared’ [ 66 , 71 , 77 ]. However this was not experienced universally - many experienced mothers facing difficulties [ 9 , 73 , 80 ]. The COVID-19 pandemic and associated public health restrictions across high-income countries disrupted access and quality of care for many pregnant and postpartum women.

Reduced health service capacity and the transition to remote and virtual care due to pandemic restrictions have been heavily criticised [ 8 , 88 ]. In many contexts, women had not received high quality maternity care during the pandemic and described overtly negative experiences [ 35 , 89 , 90 ]. Women were unable to access usual supports, had limited birth choices and reduced postpartum care which resulted in stress and anxiety. These are clearly widespread experiences, regardless of context, and highlights some of the structural weaknesses and vulnerabilities of maternity care systems. This was evident in the findings for pregnant and postpartum women of culturally and linguistically diverse backgrounds. The lack of culturally appropriate care, including access to interpretation services, doulas and being unable to have their support person present are known to impair maternal health and wellbeing [ 56 , 63 , 80 ]. These factors are key elements of respectful maternity care as they help provide information, enable women’s agency and ensure emotional and social support is available [ 91 , 92 ]. Health restrictions should not limit this service for women during times of unrest, as women and babies thrive in culturally respectful maternity services [ 93 ]. We note however that CALD women continue to be an under-represented group - only three of the 36 sampled studies reported evidence specifically for CALD groups [ 56 , 63 , 80 ]. The lack of diverse perspectives included in the evidence base makes it more difficult for culturally sensitive and community-responsive policies to be developed. Further research with women from diverse backgrounds are warranted to ensure they are not unduly disadvantaged in future pandemics [ 94 ].

A key finding was the reduced presence of partner and social support throughout the pregnancy and postpartum periods. Partner support and strong connections with extended support networks reduces stress and anxiety, and can be a positive influence on the woman and her experience [ 95 , 96 , 97 ]. In the trade-off between the risk of transmission and spread of disease, expectant fathers and partners were frequently left out [ 98 , 99 ]. Similarly, studies of families and partners of intensive care unit patients during the COVID-19 pandemic reported being physically and emotionally unable to support partners and families [ 100 , 101 ]. Close family members are essential to the recovery of patients upon discharge and partners are integral to a safe and positive pregnancy, intrapartum and postpartum experience for mothers. To ensure that maternity care services can adequately respond in the future, recommendations for some degree of flexibility for women given the long-term psychosocial impact that a negative experience would have on the woman and family unit has been sought [ 8 , 87 , 88 ].

Pregnant and postpartum women’s experiences were not universally negative. Another key finding in this review highlights the resilience and optimism that some women felt. Some women perceived this time as a “blessing in disguise” – referencing the ability to stay at home, having fewer disruptions to breastfeeding, and embracing newfound time as a family unit [ 64 , 66 , 71 ]. Coping strategies reported in this study are supported by other evidence of protective factors against stressors of the COVID-19 pandemic [ 102 , 103 , 104 ].

Maternity care services need to continue delivering care during public health emergencies. There is no possibility of delaying or postponing care; and women require care over an extended period of time. Enforced lockdowns limited movement and fear of contracting the virus in hospitals lead to delays in healthcare seeking (e.g. when there is reduced fetal movements). The pandemic altered the provision of services and women’s access to care and, as a result, some countries have reported changes to the incidences of stillbirth and preterm birth [ 105 , 106 , 107 ].

Understanding women’s experiences, their preferences and satisfaction with maternity care services are essential to a safe and positive pregnancy, labour and childbirth and postpartum period. Many maternity models of care such as woman-centred and midwifery-led care places the woman at the centre of care and her experience, focusing on woman’s health needs, expectations and aspirations [ 108 , 109 ]. These models have proven to return high levels of satisfaction and are beneficial to the psychological and physiological recovery of the woman [ 110 , 111 ]. The COVID-19 pandemic has disrupted these models of care for women who were pregnant and gave birth during the pandemic. Pressures on the maternity care system and service delivery did not facilitate the midwife-woman relationship, resulting in poorer clinical outcomes [ 112 ]. Supporting women throughout their perinatal period is essential so women and their babies are able to emerge from the experience feeling prepared, safe and satisfied [ 113 , 114 ].

Moving forward, as maternity care systems adapt to a post-pandemic structure, considerations need to be made to ensure maternity services can adequately respond to future health crises. Our QES has shown that the impacts of COVID-19 went far beyond the direct impacts on women who were infected with SARS-COV2. All women giving birth over the pandemic, especially in the first two years, were indirectly impacted and as a result experienced a lack of autonomy during their pregnancy and childbirth, barriers to accessing face-to-face care and loss of social supports. This highlights the need to consider women’s views and experiences in developing policies for future responses to pandemics or public health emergencies.

We recommend that policy makers and maternity care services should: 1) optimise care delivery to maintain face-to-face care when possible and facilitate the presence of chosen support people; 2) enhance communication channels between maternity care services and women to minimise misinformation, stress and anxiety; and 3) support social and mental wellbeing to ensure women have access to adequate social support and mental health services are well resourced.

Strengths and limitations

The rigorous and systematic methodology of this QES in selecting studies for inclusion allowed us to analyse experiences of a heterogenous cohort of pregnant and postpartum women during the COVID-19 pandemic. When we started the review, the abundance of published work of women’s experiences was overwhelming, therefore strict eligibility criteria were used to ensure that findings could be obtained and compared across studies. This study was therefore limited to experiences of women in high-income countries and cannot be generalised to low- and middle-income countries.

Studies were subject to a sampling framework to ensure that a diverse, yet data rich sample of studies contributed to the development of review findings. This had its own set of limitations as the sampling framework is not a validated tool and may be biased by the user’s own interpretation. Additionally, the search strategy was limited to the first two years of the pandemic. While it is possible research was published outside of this two-year period, we felt that it was unlikely that different experiences would be reported. An updated search (December 2022) was conducted to determine if any new themes emerged, however no new themes emerged and therefore did not warrant the addition of any new studies. Almost all studies that used interviews to collect qualitative data did so via remote methods. Telephones and video conferencing tools were popular methods to conduct interviews, adhering to social distancing guidelines. Whilst this increased accessibility for participants from diverse geographical locations, there may be concerns about the depth of data obtained and exclusion of participants that are unable to access these technologies. A further consideration is the limited number of studies exploring the experiences of women from diverse backgrounds. This prevented us from more critically examining what factors and circumstances shape women’s experiences and responses.

Women’s pregnancy and postpartum experience during the COVID-19 pandemic showcased similarities despite different contexts. This QES has collated the experiences of women from high income countries sharing insight into the challenges faced and resilience of pregnant and postpartum women. The COVID-19 pandemic has exacerbated many systemic shortfalls of the maternal and newborn health system – a system that is essential to the health and wellbeing of women and babies. The review findings have highlighted areas within this period where strategies to inform policy and practice could be optimised to allow for better access to care and support for women in their journey to motherhood. Future pandemic preparedness strategies need to maximise face-to-face care, optimise communication channels to combat misinformation and anxiety, include a flexible approach to public health restrictions for women and their families by allowing formal and informal support networks to be readily available and accessible, and to ensure maternal mental health is a priority.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files]. Additional information is available from the corresponding author on reasonable request.

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The primary author is funded by Deakin University Postgraduate Research Scholarship as a PhD Candidate. AB is supported by the Australian Government Research Training Program, CSEH is supported by the Australian National Health and Medical Research Council Leadership Investigator Grant, and JPV is supported by the Australian National Health and Medical Research Council Emerging Leadership Investigator Grant. The funding bodies had no role in the conceptualisation of the study design and data collection, data analysis, interpretation and writing of the manuscript.

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Tan, A., Blair, A., Homer, C.S. et al. Pregnant and postpartum women’s experiences of the indirect impacts of the COVID-19 pandemic in high-income countries: a qualitative evidence synthesis. BMC Pregnancy Childbirth 24 , 262 (2024). https://doi.org/10.1186/s12884-024-06439-6

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Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

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.