what is data saturation in research

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What is data saturation in qualitative research.

8 min read A crucial milestone in qualitative research, data saturation means you can end the data collection phase and move on to your analysis. Here we explain exactly what it means, the telltale signs that you’ve reached it, and how to get there as efficiently as possible.

Author:  Will Webster

Subject Matter Expert:  Jess Oliveros

Data saturation is a point in data collection when new information no longer brings fresh insights to the research questions.

Reaching data saturation means you’ve collected enough data to confidently understand the patterns and themes within the dataset – you’ve got what you need to draw conclusions and make your points. Think of it like a conversation where everything that can be said has been said, and now it’s just repetition.

Why is data saturation most relevant to qualitative research? Because qualitative research is about understanding something deeply, and you can reach a critical mass when trying to do that. Quantitative research, on the other hand, deals in numbers and with predetermined sample sizes , so the concept of data saturation is less relevant.

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How to know when data saturation is reached

At the point of data saturation, you start to notice that the information you’re collecting is just reinforcing what you already know rather than providing new insights.

Knowing when you’ve reached this point is fairly subjective – there’s no formula or equation that can be applied. But there are some telltale signs that can apply to any qualitative research project .

When one or multiple of these signs are present, it’s a good time to begin finalizing the data collection phase and move on to a more detailed analysis.

Recurring themes

You start to notice that new data doesn’t bring up new themes or ideas. Instead, it echoes what you’ve already recorded.

This is a sign that you’ve likely tapped into all the main ideas related to your research question.

No new data

When interviews or surveys start to feel like you’re reading from the same script with each participant, you’ve probably reached the limit of diversity in responses. New participants will probably only confirm what you already know.

You’ve collected enough instances and evidence for each category of your analysis that you can support each theme with multiple examples. In other words, your data has become saturated with a depth and richness that illustrates each finding.

Full understanding

You reach a level of familiarity with the subject matter that allows you to accurately predict what your participants will say next. If this is the case, you’ve likely reached data saturation.

Consistency

The data starts to show consistent patterns that support a coherent story. Crucially, inconsistencies and outliers don’t challenge your thinking and significantly alter the narrative you’ve formed.

This consistency across the data set strengthens the validity of your findings.

Is data saturation the goal of qualitative research?

In a word, no. But it’s often a critical milestone.

The true goal of qualitative research is to gain a deep understanding of the subject matter; data saturation indicates that you’ve gathered enough information to achieve that understanding.

That said, working to achieve data saturation in the most efficient way possible should be a goal of your research project.

How can a qualitative research project reach data saturation?

Reaching data saturation is a pivotal point in qualitative research as a sign that you’ve generated comprehensive and reliable findings.

There’s no exact science for reaching this point, but it does consistently demand two things: an adequate sample size and well-screened participants.

Adequate sample size

Achieving data saturation in qualitative research heavily relies on determining an appropriate sample size .

This is less about hitting a specific number and more about ensuring that the range of participants is broad enough to capture the diverse perspectives your research needs – while being focused enough to allow for thorough analysis.

Flexibility is crucial in this process. For example, in a study exploring patient experiences in a hospital, starting with a small group of patients from various departments might be the initial plan. However, as the interviews progress, if new themes continue to emerge, it might indicate the need to broaden the sample size to include more patients or even healthcare providers for a more comprehensive understanding.

An iterative approach like this can help your research to capture the complexity of people’s experiences without overwhelming the research with redundant information. The goal is to reach a point where additional interviews yield little new information, signaling that the range of experiences has been adequately captured.

While yes, it’s important to stay flexible and iterate as you go, it’s always wise to make use of research solutions that can make recommendations on suggested sample size . Such tools can also monitor crucial metrics like completion rate and audience size to keep your research project on track to reach data saturation.

Well-screened participants

In qualitative research, the depth and validity of your findings are of course totally influenced by your participants. This is where the importance of well-screened participants becomes very clear.

In any research project that addresses a complex social issue – from public health strategy to educational reform – having participants who can provide a range of lived experiences and viewpoints is crucial. Generating the best result isn’t about finding a random assortment of individuals, but instead about forming a carefully selected research panel whose experiences and perspectives directly relate to the research questions.

Achieving this means looking beyond surface criteria, like age or occupation, and instead delving into qualities that are relevant to the study, like experiences, attitudes or behaviors. This ensures that the data collected is rich and deeply rooted in real-world contexts, and will ultimately set you on a faster route to data saturation.

At the same time, if you find that your participants aren’t providing the depth or range of insights expected, you probably need to reevaluate your screening criteria. It’s unlikely that you’ll get it right first time – as with determining sample size, don’t be afraid of an iterative process.

To expedite this process, researchers can use digital tools to build ever-richer pictures of respondents , driving more targeted research and more tailored interactions.

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Exploring data saturation in qualitative research

Last updated

24 March 2023

Reviewed by

Miroslav Damyanov

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• What is data saturation in qualitative research?

The concept of saturation relates to the number of interviews conducted in qualitative research. Data saturation occurs in the research process when you’ve collected sufficient data to draw the necessary conclusions, and collecting any further data won't produce value-added insights. 

The term data saturation originated from qualitative research's grounded theory. Grounded theory is a broad research approach first introduced by sociologists Glaser and Strauss in the 1960s. 

Sample sizes in qualitative research

It’s important to understand sample sizes in qualitative research before discussing data saturation in qualitative research. Qualitative research focuses on themes, unlike quantitative research , which involves the collection and analysis of numerical data.

You can gather qualitative data via observation, interviews, and task completion. A key element in qualitative research is small sample sizes that are homogeneous in nature. Qualitative research focuses on segmenting audiences into similar psychographic traits instead of interviewing a population with a broad range of traits. 

This guarantees that a research study focuses on exploring ideas or themes from a particular subset of a population. The idea is to ensure that segments have well-defined traits which are screened during recruitment. Since there will be well-defined segments, qualitative researchers focus on addressing a strictly defined number of participants to explore themes and ideas. 

How many participants should a qualitative study have?

However, it's important to note that researchers should maintain a rigorous recruitment process to ensure the integrity of the study, regardless of the sample size. Overall, the goal of qualitative research should be to achieve saturation, which occurs when researchers begin to observe the same themes and patterns repeatedly, regardless of the number of participants interviewed or observed.

Data saturation in qualitative interviews

Data saturation means that researchers aren't finding any additional data from interviews. In most cases, researchers go out of their way to seek groups that stretch data diversity as far as possible just to ensure that saturation is based on the broadest possible range of data on the category. 

• What influences data saturation?

Parameters and factors that influence saturation in focus group data include:

Study purpose

Type of codes

Type and degree of saturation

Group stratification

The number of groups per stratum 

• Hybrid forms of saturation

Different experts have adopted several interpretations of saturation combining two or more models of saturation, making its conceptualization less distinct. Common modes of saturation include:

Data saturation . Its principal focus is data collection , and it relates to the degree to which new data repeat what was mentioned in the previous data.

A priori semantic saturation . Its principal focus is sampling, and it relates to the degree to which identified themes or codes are exemplified in the data.

Inductive thematic saturation . Its principal focus is analysis, and it relates to the emergence of new themes or codes.

Theoretical saturation . Its principal focus is sampling, and it relates to developing theoretical categories. It is also related to grounded theory methodology. 

Different experts' views on saturation seem to embody different elements of saturation. Experts such as Hennink MM have created hybrid saturation by combining elements of all four saturation models. 

• When should you seek saturation?

The perspective that a researcher takes on what saturation means within a particular study will have implications for when they seek saturation. For instance, you can identify saturation at an earlier stage in the research process if a researcher considers the data saturation approach. This is because, from this perspective, you typically view saturation as separate from formal analysis. 

When researchers consider inductive thematic saturation, the fact that researchers focus on reaching saturation levels at the analysis level might suggest that they will achieve saturation at a later stage than in data saturation approaches. On the other hand, theoretical saturation indicates that the analysis process is often at a more advanced stage and at a higher level of theoretical generality. 

• How can you measure saturation?

Ways to measure saturation in qualitative research include:

Reliance on probability theory or the assumption of a random sample

Retrospective assessment dependent on having a fully coded/analyzed data set

Lack of comparability in metrics 

• Approaches to assessing saturation

You can use different strategies to assess saturation in qualitative research. These include:

Code frequency counts 

This approach involves counting codes in successive transcripts or sets of transcripts until new code frequency diminishes, signaling the reach of saturation. 

Code meaning 

This approach focuses on reaching a full understanding of issues in data as the indicator that you’ve achieved saturation by assessing whether the issue, its nuances, and dimensions are completely identified and understood. 

Comparative method

This method adds a more structured comparison to the code frequency counts approach. It involves reviewing data in pre-determined batches and listing all new codes in a saturation table for each data batch. 

High-order groupings 

This approach involves counting higher-order groupings of codes like salient themes, meta themes, or categories. 

Stopping criterion 

Under this approach, you add a stopping criterion to the code frequency count approach. This approach involves reviewing initial interview samples or focus groups to identify new codes. It also involves using a pre-determined stopping criterion, which is typically the number of consecutive groups/interviews after the initial sample where you identified no new codes in the sample. You accomplish saturation when you identify no new codes after the stopping criteria of interviews after the initial sample. 

• Final thoughts

Achieving saturation shouldn't be an easy ground for ending your study right away. If you arrive at saturation quickly, first ask yourself whether you've covered the audience for the product/idea.

If not, recruit more participants who fit your segment and then test the ideas/product extensively. However, if you've interacted with many participants and don't discover any new themes consecutively, it may be wise to stop at the number of participants you’re at. 

Other variables may affect saturation, such as the quality of your recruiting, the overall focus of your objectives, and how you ask your study questions. Ensure you understand saturation before discovering its role in effective qualitative research.

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What is Data Saturation in Qualitative Research?

• February 2, 2022

Article Summary:  Data saturation is a principle in qualitative research whereby you can have significantly smaller sample sizes than in quantitative research. This is because of “data saturation” which means you start to hear the same themes repeatedly in a research interview.

Whether you’re in academia or in business, and working in qualitative research , you may have come across or be familiar with the term “ saturation ” in qualitative research. It’s an important principle, and actually one of the defining characteristics of qualitative research – since qualitative research deals with small sample sizes – so we want to dedicate a post to saturation in qualitative research.

First, let’s discuss sample sizes in qualitative research

Before we dive into saturation in qualitative research, we first need to define qualitative research, and specifically, discuss sample sizes in qualitative research. Unlike quantitative research, which is rooted in statistical analysis and seeks to analyze “how many” or patterns in data, qualitative research focuses on themes. Qualitative data is collected through interviewing, observation, and sometimes task completion. The most common methodologies used in market and UX research are in-depth interviews, focus groups , ideation groups, dyads, triads , ethnographies, and social listening studies.

A key component of qualitative research is smaller sample sizes that are homogenous in nature. This means that instead of interviewing a population with a wide-array of characteristics, qualitative research first focuses on segmenting audiences into similar psychographic qualities (often called “personas.”) This ensures that the research study is aimed at exploring themes or ideas from a specific subset of a population. For example, a segment might be “small business owners who use Brand X to order supplies for their business.” The idea is to ensure that the segments have well-defined characteristics, which are screened during the recruiting process.

Since there will be highly defined segments, qualitative researchers focus on speaking to a defined number of participants in order to explore themes.

How many participants should a qualitative study have?

We’ve written on this topic of sample sizes before, so we won’t spend this post on that, but typically (again, for homogenous populations), between 10-20 total participants, per segment, is a solid number. Really, what will be the cutoff is when you hit saturation in your research, so let’s focus on what saturation is.

Saturation in qualitative research is when, through the course of interviewing (or observation), you notice the same themes coming out, repeatedly. As you interview more and more participants, you stop finding new themes, ideas, opinions, or patterns. Essentially, saturation is when you get diminishing return, despite talking to more and more people.

How soon will you hit saturation? That depends, of course. For highly homogenous samples (very niche industries/job roles) for examples, saturation can happen after as little as 5 interviews. If you have a more diverse population sample (teenagers who use a particular social media app for 20+ hours a week, for example), you may need to interview 30 or more people before you hit saturation of themes.

Saturation can also depend on the specificity of the study. For example, if you are doing a UX study and asking people to test out an app, you may find pretty quickly (after maybe 4-5 interviews) that everyone is having the same reaction or moving through the product in the same way. You’re studying a specific task with defined variables, so saturation is likely to happen sooner.

However, if you are running an ideation workshop, testing reactions to advertising, or studying complex products, saturation will take longer: you may need to talk to 20+ participants before you see those patterns become really defined and you feel you’ve reached saturation, by exploring all of the available themes.

Saturation is extremely important – pay attention to it

If you find that you’ve reached saturation very quickly, don’t necessarily cut the study short right away. First ask: Have we thoroughly covered the audience for this idea/product? If not, recruit additional participants who fit your segment, and test their ideas. Conversely, if you’ve spoken to 45 people and have not heard anything new in the previous 10 interviews, likely you won’t be uncovering too many new themes, so it may be wise to stop at the number you’re currently at.

There are other variables involved in saturation, such as the quality of your recruiting, how you ask your questions (your discussion guide), and the overall focus of your objectives. We will cover those in subsequent posts. For now, we hope you have an understanding of what saturation is and why it’s so critical in qualitative research.

If you want to learn more about conducting qualitative research, check out our training programs from InterQ Learning Labs.

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Data Saturation in Qualitative Research

In this blog post, learn what data saturation is, how it relates to qualitative research practices, and how to leverage quantilope's video research solution: inColor. 

Table of Contents: 

What is data saturation in qualitative research.

• Does data saturation matter? 
• Data collection to reach data saturation 
• Methodologies used to reach data saturation

qualitative data analysis with quantilope's inColor

Data saturation is the point in a research process where enough data has been collected to draw necessary conclusions, and any further data collection will not produce value-added insights. Data saturation is a term that originates from qualitative research’s grounded theory, a broad research method first coined in the 1960s by sociologists Glaser and Strauss.

Glaser and Strauss’ grounded theory describes the way in which social research reveals patterns in data that can then be used to generate theories and hypotheses on which further research can be done (this is in contrast to quantitative research, where pre-existing hypotheses form a framework for research).

In their 1967 book 'The Discovery of Grounded Theory,' Glaser and Strauss describe the concept of saturation like this:

 'The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation. Saturation means that no additional data are being found whereby the sociologist can develop properties of the [theoretical] category. As he sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated.'

In other words, when the number of interviews, focus groups or other qualitative method is large enough, data analysis will start showing the same themes, with no new findings or variability, however thorough the analysis.

Back to Table of Contents

Does data saturation matter?

There has been a lot of debate and disagreement amongst social sciences professionals and researchers around the importance of data saturation. One reason for this is that qualitative research studies vary in their end goals; some projects will require exploring all possible avenues in great detail, while others are looking for much less exhaustive studies.

It's true that in any qualitative research study, the researcher wants to be sure that the project obtains the information it sets out to discover. For some studies, this might mean a broad research question pertaining to the topic - for example, 'what are the main concerns that people in the US have about the world today?'. It's a broad subject, and for a CPG business wondering how best to position its product, it might be enough to know that personal finances and climate change are pretty high up in importance and that the pricing and eco-credentials of its product need to be in line with needs and expectations relating to those concerns (competitive pricing within the category and recyclable packaging, for example.)

However, if a media company asks the same research question, the depth of detail required might be greater. Within the broad themes that emerge, precise and detailed sub-categories of concern under those themes might be required to tailor news and commentary to the interest of the audience.

Another point of contention is that data saturation focuses on the number of research interviews (aka, sample size ) rather than the quality of the data collection. A high number of depth interviews could be recruited and responses might start to repeat across the sample, but if the information extracted isn't rich enough then important insights can be missed. In an ideal study, a mix of both quantity and quality will be achieved.

Data collection to reach data saturation

qualitative data collection offers a highly flexible way to explore topics of interest. The value in qualitative research lies in how well qualitative inquiries are collected, with in-depth probing and steering of the conversation towards the most useful insights. This, as with sampling, comes with experience and good training in qualitative data collection techniques .

To feel confident any qualitative research outcome will provide adequacy for all pertinent insights to be unearthed, researchers need to ensure two things in their data collection:

• Adequate sample size
• Research subjects and quality of responses are interrogated thoroughly 

The sample size of a qualitative research study really depends on the research questions at hand, and the nature of the information sought. For example, do researchers need just a few simple soundbite citations to support their research initiatives, or do they need in-depth quotes from case studies with specific recalled experiences?

Following qualitative research fieldwork, the analysis of responses is key to what is known as inductive thematic saturation: when the emergence of new themes and new codes has plateaued. When you're conducting thematic analysis and you're starting to hear the same responses come up again and again with nothing new emerging, then you're probably at the point of saturation.

Methodologies used to reach saturation

Knowing what data saturation is, and best practices to keep in mind when collecting qualitative data, there are various methods a qualitative researcher can leverage during the study design process.

Qualitative research has traditionally been thought of as individual interviews but has expanded over time to include a whole host of other methodologies, including field methods, focus groups, video diaries, written diaries, ethnography observation exercises, and so on, all of which are valued for the unique angles they can deliver on a topic.

quantilope's qualitative research solution, inColor , offers an instinctive platform that brings you face-to-face with your target market for video qualitative interviews. 

Setting up qualitative studies with inColor puts you in charge of the number of participants you would like to include, with the option to add more participants as the study progresses. You can watch and listen to videos that participants create, which in itself brings data collection to life to get a good sense of views and reactions that are emerging. However, the analysis isn't just left to the researcher; multiple AI-driven analyses ensure that keywords, sentiments, and emotions are identified so that new themes and their relative importance are always uncovered - helping to easily identify any point of saturation. This results in truly insightful, conceptual, qualitative data that can be applied to your business immediately. 

If you'd like to know more about qualitative market research with quantilope and how you can be sure you've got all bases covered with your qual insights get in touch with us below: 

Get in touch to learn more!

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What is Data Saturation? Grasp its uses in Qualitative Research

Have you ever wondered what is data saturation? Learn its importance, and how it enhances the trustworthiness of findings.

In the realm of qualitative research, data saturation plays a crucial role in ensuring the validity and trustworthiness of findings. It is a concept that researchers employ to determine the point at which collecting additional data no longer provides new insights or information. In this article, we will delve into the meaning of data saturation, explore its significance in qualitative research, discuss factors influencing saturation, and highlight approaches to measuring and assessing it. By understanding data saturation, researchers can enhance the quality and rigor of their studies.

What is Data Saturation?

Data saturation refers to the point in qualitative research where collecting new data ceases to generate novel insights or themes. It is the stage where researchers achieve a sufficient depth and breadth of information, enabling them to confidently draw conclusions and develop theories from their data. In other words, it represents the saturation of themes or categories within the dataset, indicating that little or no new information is emerging.

Factors Influencing Data Saturation

Several factors influence data saturation in qualitative research. These factors can vary depending on the research context and the nature of the data collected. Some key factors to consider include:

Sample size

The size of the participant sample plays a role in achieving data saturation. Generally, a larger sample size increases the likelihood of reaching saturation as it allows for a wider range of perspectives and experiences to capture.

Data collection methods

The choice of data collection methods, such as interviews, focus groups, or observations, can influence data saturation. Each method has its strengths and limitations in terms of generating rich and diverse data.

Researcher expertise

The knowledge and expertise of the researcher can influence data saturation. A skilled researcher who is well versed in the research topic can recognize patterns and themes more effectively, potentially reaching saturation sooner.

Hybrid Forms of Data Saturation

In some cases, researchers employ hybrid forms of saturation to enhance the validity and reliability of their findings. These approaches involve combining multiple data sources or methods to gather a comprehensive understanding of the research topic. By triangulating data from different sources, such as interviews, observations, and document analysis, researchers can strengthen their conclusions and ensure data saturation from various angles.

When and How to Seek Data Saturation

Seek for Data saturation begins after the collection of a substantial amount of data. Researchers must continuously analyze and interpret the data during the research process to identify emerging themes and to reach saturation. It is important to note that data saturation is not always a predetermined goal but rather a point of confidence where the researcher feels that additional data will not significantly contribute to the findings.

To seek saturation effectively, researchers can:

• Engage in iterative data collection and analysis : Iterative processes of collecting and analyzing data allow researchers to refine their research questions and sampling strategies as new insights emerge. This iterative approach helps in reaching saturation by ensuring that diverse perspectives and experiences are adequately represented.
• Conduct member checks : Member checks involve sharing findings or interpretations with participants to validate the accuracy and comprehensibility of the data. This process helps ensure that the researchers’ understanding aligns with the participants’ experiences, enhancing the trustworthiness of the data.

Measuring Data Saturation

While data saturation is a qualitative concept, researchers often seek ways to measure and demonstrate saturation in their studies. Although there is no standardized method for quantifying saturation, researchers can employ various strategies to provide evidence of saturation:

Theoretical saturation

This approach involves determining saturation based on the degree of theoretical insights obtained from the data. Researchers assess whether the emerging themes and patterns adequately explain the phenomenon under investigation.

Saturation grids or matrices

Researchers can create grids or matrices to track the appearance and recurrence of themes across different data sources. This visual representation allows them to identify when saturation is achieved for specific themes or categories.

Assessing Saturation: Different Approaches

Assessing saturation involves evaluating the quality and sufficiency of the data to draw meaningful conclusions. Researchers can employ different approaches to assess saturation:

Peer debriefing

Researchers can engage in discussions with colleagues or experts in the field to review and validate their interpretations. This external feedback helps ensure that saturation has been adequately achieved and enhances the credibility of the research.

Methodological transparency

Clearly documenting the data collection and analysis processes helps establish the trustworthiness of the findings. Researchers should provide detailed descriptions of the steps taken to reach saturation, allowing others to assess the rigor of the study.

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What Influences Saturation? Estimating Sample Sizes in Focus Group Research

Monique m. hennink.

Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA

Bonnie N. Kaiser

Department of Anthropology, University of California, San Diego (previous)

Duke University (current)

Mary Beth Weber

Hubert Department of Global Health, Rollins School of Public Health, Emory University

Saturation is commonly used to determine sample sizes in qualitative research, yet there is little guidance on what influences saturation. We aimed to assess saturation and identify parameters to estimate sample sizes for focus group studies in advance of data collection. We used two approaches to assess saturation in data from 10 focus group discussions. Four focus groups were sufficient to identify a range of new issues (code saturation), but more groups were needed to fully understand these issues (meaning saturation). Group stratification influenced meaning saturation, whereby one focus group per stratum was needed to identify issues; two groups per stratum provided a more comprehensive understanding of issues, but more groups per stratum provided little additional benefit. We identify six parameters influencing saturation in focus group data: study purpose, type of codes, group stratification, number of groups per stratum, and type and degree of saturation.

Introduction

Selecting an appropriate sample size for qualitative research remains challenging. Since the goal is to select a sample that will yield rich data to understand the phenomenon studied, sample sizes may vary significantly depending on the characteristics of each study. The concept of saturation is the most common guiding principle to assess the adequacy of data for a purposive sample ( Morse 1995 ; 2015 ). Saturation was developed by Glaser & Straus (1967) in their grounded theory approach to qualitative research, which focuses on developing conceptual or explanatory models from textual data. Within this context, theoretical saturation (also called theoretical sampling) is used. Theoretical sampling is both continuous and data-driven, involving an iterative process of concurrent sampling, data collection, and data analysis to determine further data sources ( Charmaz 2014 ) and continuing until all constructs of a phenomenon are explored and exhausted to support an emerging theory ( Glaser and Strauss 1967 ). In grounded theory, theoretical saturation therefore focuses on the adequacy of a sample to provide conceptual depth and richness to support an emerging theory, rather than on sample size per se ( Corbin and Strauss 2015 ; Birks and Mills 2011 ).

However, the term saturation has become part of the broader lexicon of qualitative research (O’Reilly and Parker 2012). It is now widely used outside of its grounded theory origins and has become “the most frequently touted guarantee of qualitative rigor” ( Morse 2015 , p.587). In this broader application, it is often called data saturation or thematic saturation and refers to the point in data collection when issues begin to be repeated and further data collection becomes redundant i . This broader use of saturation focuses more directly on assessing sample size rather than the adequacy of data to develop theory (as in theoretical saturation). Yet, it remains unclear what saturation means when used outside of grounded theory, how it can be assessed, and what influences saturation for different qualitative methods, types of data, or research objectives (Nelson 2017; O’Reilly and Parker 2012; Kerr et al 2010 ). Identifying influences on saturation in this broader context can be used by researchers, reviewers, ethical review boards, and funding agencies to determine effective sample sizes in qualitative research proposals.

Determining the number of focus group discussions needed in a study is a key part of research design, as sample size influences multiple study components (e.g., budget, personnel, timetable). However, saturation (and therefore sample size) cannot be determined in advance, as it requires the review of study data. There is limited methodological research on parameters that influence saturation to assist researchers in selecting an effective sample size in advance of data collection ( Morse 1995 ; Guest et al 2006 ; Kerr et al 2010 ; Carlsen & Glenton 2011 ; Bryman 2012 ). Guidance is needed on how to estimate saturation prior to data collection to identify an effective sample size for qualitative research proposals. In this study, we aim to assess saturation in data from focus group discussions and identify parameters to estimate sample sizes for focus group studies in advance of data collection. This goal contributes to continual calls for an evidence base of empirical research on saturation ( Morse 1995 ; Guest et al 2006 ; Kerr et al 2010 ; Carlsen & Glenton 2011 ).

Assessing Saturation in Focus Group Research

There is a growing concern in qualitative research over researchers claiming to have reached saturation in qualitative studies without providing adequate explanation or justification on how saturation was assessed or achieved ( Hennink, Kaiser & Marconi 2016 ; Malterud et al 2015; Bowen 2008 ; Guest, Bunce & Johnson 2006 ; Morse, 1995 , 2000 , 2015 ). Carlsen and Glenton (2011) conducted a systematic review of 220 studies using focus group discussions to identify how authors justified their sample size. They found that of those studies that explained the sample size, 83% used saturation to justify the number of focus groups in the study, but few authors stated clearly how saturation was assessed or achieved, and many included unsubstantiated claims of saturation and reported reaching saturation while still using the predetermined number of focus groups. Guest et al (2016) similarly reviewed 62 textbooks on qualitative research or focus group methodology and found that 42 provided no guidance at all on the number of focus group discussions needed for a study, 6 recommended saturation, 10 gave a numeric recommendation, and 4 mentioned saturation and provided a suggested number of focus groups. The sample size recommendations ranged widely from 2 to 40 groups, with a commonly cited guideline to conduct at least two focus groups for each defining demographic characteristic. A critical outcome of this review is that none of the recommendations on sample sizes were supported by empirical data demonstrating when saturation is achieved in focus group research. These reviews highlight a significant gap in the methodological literature on empirical research assessing saturation and sample size guidelines for focus group research.

There is a small but emerging body of methodological literature assessing saturation in studies using in-depth interviews ( Hennink et al 2016 ; Francis et al 2010 ; Guest et al, 2006 ). However, there are few methodological studies where the authors assess saturation and provide guidance on sample sizes for focus group research. Guest et al (2016) used data from a study with 40 focus group discussions to develop empirically based recommendations on sample sizes for focus group studies. In this study, the authors documented the process of identifying codes to ascertain when each code was developed and then determined the number of focus groups needed to identify 80% and 90% of thematic codes across the study. They assessed code frequency across data as a proxy for salience of themes and accounted for any temporal bias in identifying codes by randomizing the order of focus groups and replicating their analyses of saturation. Results showed that 64% of themes were generated from the first focus group, 84% by the third focus group and 90% by the sixth group. This pattern remained regardless of the order in which focus groups were reviewed. Three focus groups were also enough to identify the most prevalent themes across these data. The authors conclude that when averaging the sequential and randomized order of focus groups, two to three focus groups are sufficient to capture 80% of themes, including the most prevalent themes, and three to six groups for 90% of themes in a homogenous study population using a semi-structured discussion guide ( Guest et al 2016 ).

In an earlier study, Coenen et al (2012) assessed saturation in focus group discussions by different approaches to developing codes: an inductive approach of identifying themes from the data itself and a deductive approach of applying themes to data from an existing theoretical framework. The authors used maximum variation sampling to create a diverse sample of participants, which differs from the largely homogenous sample used by Guest et al (2016) . Saturation was defined as the point at which linking concepts of two consecutive focus groups revealed no additional second-level categories. The authors deemed that saturation occurred at five focus groups, regardless of the approach to code development.

It is difficult to compare results of these two studies given that they operationalize saturation differently – percentage of codes identified across data ( Guest et al 2016 ) and absence of new category development in consecutive focus groups ( Coenen et al 2012 ). Nonetheless, across both studies, saturation is achieved by six focus groups. The findings of these studies are significant, as they demonstrate that saturation is achieved at a relatively small number of focus groups, compared with typical guidance given in methodology textbooks that is not empirically based. Taken together, these studies begin to contribute an understanding of saturation in focus group research using homogenous and diverse samples, amongst inductive and deductively derived codes, and saturation in codes versus categories. Despite the differences in the type of sample, type of codes assessed, and operationalization of saturation, both studies reached saturation at a relatively similar number of focus groups.

However, there are two important limitations of these studies. First, the assessment of saturation is based on identifying the occurrences of new themes, without also assessing the understanding of these themes across the data. Identifying the presence of themes is only the first step in reaching saturation. The first time a theme is identified in data may not be detailed or insightful; therefore, additional data may be required to fully capture the meaning of the issue and to understand the depth, breadth, and nuance of the issue ( Kerr et al 2010 ; Hennink et al 2016 ). Thus, the authors of these studies provide no guidance on sample sizes needed to reach saturation in the meaning of issues in data. Second, they hardly acknowledge the group format of data collection in focus groups and how this may influence saturation. Focus group discussions involve non-directive interviewing whereby group participants engage in discussion, which generates a different type of data than interviews with a single participant due to the interaction and spontaneity of the group dialogue ( Morgan, 1997 ; Krueger and Casey 2015 ). The group format has potential to generate a broad range of issues and perspectives, but it may also sacrifice narrative depth and understanding of issues. It is unclear how these elements of focus group discussions influence saturation. Finally, there is no examination of how group composition or demographic stratification of focus groups influence saturation. Assessing how these design elements of focus group research influence sample size and saturation is critically important yet is omitted from current literature.

In this study, we aimed to assess saturation in focus group data and identify parameters to estimate sample sizes for focus group studies in advance of data collection. We utilize the broader application of saturation used outside the grounded theory approach, as described above. This focus is important given that saturation is commonly applied to a wide range of research approaches without adequate description or justification of how it was applied or achieved.

We use two approaches to assess saturation that we developed in an earlier study – code saturation and meaning saturation ( Hennink et al 2016 ) – to assess saturation in focus group data. First, we assessed the sample size needed to reach code saturation, which we define as the point when no additional issues are identified in data and the codebook has stabilized. Second, we assessed the sample size needed to reach meaning saturation, which we define as the point at which we fully understand the issues identified and when no further insights or nuances are found. We then examined code and group characteristics to identify parameters that influence saturation in focus group data. We sought to answer the following specific research questions in this study:

• How many focus group discussions are needed to reach code saturation?
• How many focus group discussions are needed to reach meaning saturation?
• How do code characteristics and focus group composition influence saturation?
• What parameters can be used to assess saturation a priori in focus group research?

Study Background

We used data from the South Asian Health and Prevention Education (SHAPE) study for our analyses ( clinicaltrials.gov # {"type":"clinical-trial","attrs":{"text":"NCT01084928","term_id":"NCT01084928"}} NCT01084928 ). Below we provide an overview of the data collection and analysis of the broader SHAPE study as context for our analyses of saturation in these data. The SHAPE study was a planning and feasibility study to inform the design of a diabetes prevention program for South Asian Americans. South Asians (individuals from the Indian subcontinent) are at a high risk for developing diabetes, often presenting with the condition at younger ages and lower body mass indices than other race-ethnic groups ( Gujral et al 2013 ). Although there is strong evidence from randomized controlled trials showing that lifestyle education interventions can prevent or delay type 2 diabetes in high-risk populations ( Crandall et al 2008 ), there is a need to translate these programs to the South Asian population. SHAPE included a formative phase of qualitative research to inform the development and tailoring of the intervention to the needs of the South Asian community and to ensure its cultural acceptability.

Data Collection and Analysis

SHAPE data comprised focus group discussions with self-identifying South Asians adults living in Atlanta, Georgia. Participants were purposively recruited through advertisements in local South Asian magazines, health fairs, and screenings targeting South Asians, and community locations such as South Asian shopping centers and community organizations. Sixteen focus group discussions were conducted in community locations. Focus groups were stratified by age (18–39 years and 40 years or older) and sex, comprising four groups in each stratum. Focus groups lasted 60–90 minutes and were conducted in English by a trained moderator matched for sex, but not ethnicity, to the participant group. Participants were asked open-ended questions on their views of diabetes and obesity, diet and physical activity behaviors, and barriers and facilitators for a healthy lifestyle, as well as providing feedback on specific design elements of the intervention. Participants were given refreshments, travel reimbursement, and a gift bag. We used data from the first ten focus group discussions in this study, as the final 6 groups focused only on some of the discussion topics and therefore, were not suitable for our analyses of saturation. Data were collected between November 2009 and March 2010. The Emory Institutional Review Board (IRB00019630) approved the study. Individual informed consent was sought from participants before each focus group discussion. Participants were informed of the study procedures, risks and benefits and provided written consent to take part in the focus group discussions and for the audio recording.

All focus groups were digitally recorded, transcribed verbatim, de-identified, and entered into the MaxQDA program (Verbi Software, Germany) for analysis. We conducted a close reading of transcripts to identify issues raised by participants. Each issue was verified by two analysts before its inclusion in a codebook, comprising a codename and a description of each issue. A total of 50 codes were developed including both inductive codes derived from the transcripts and deductive codes originating from the discussion guide. Inter-coder agreement was assessed between two coders to compare the consistency of code use and rectify discrepancies before the whole data set was coded.

To assess saturation, we used a similar process developed in an earlier study ( Hennink et al 2016 ). For our analyses on saturation, we documented the process of code development and conducted separate analyses of these procedural data, as described in the sections below.

Assessing Code Saturation

To assess code saturation, we reviewed each focus group discussion transcript in the order in which groups were conducted and documented the development of codes. We recorded all new codes developed and their characteristics, including the code name, code definition, type of code (inductive or deductive), notes about issues with new codes (e.g., clarity of the issue captured, completeness of the code definition), and whether any previously developed codes were present in the transcript. Code definitions included a description of the issue captured, instructions for code application, and an example of text relevant to the code. To document the evolution of code development, we also recorded changes made to codes or code definitions as we proceeded, including the type of change and the focus group at which the change was made. We continued to document code development and the iterative evolution of codes for each focus group discussion until the final codebook was complete.

We then categorized codes for analysis, using the same categorizations as we developed in our earlier work on saturation ( Hennink et al 2016 ) as follows. First, codes were categorized as inductive or deductive. Inductive codes were content driven and raised by participants themselves, whereas deductive codes originated from the discussion guide and were then verified with data. Second, changes in code development were categorized as a change in code name and change in code definition (e.g. code expanded, inclusion criteria or examples added). Third, codes were also categorized as concrete or conceptual. ‘Concrete’ codes were those capturing explicit, definitive issues in data; for example, the code ‘food taste’ captured concrete discussion about the taste of food. Similarly, the code ‘family time’ captured any discussion about exercise time competing with family responsibilities. ‘Conceptual’ codes were those capturing abstract constructs such as perceptions, emotions, judgements, or feelings. For example, the conceptual code ‘denial’ captured comments about failure to recognize symptoms of diabetes, refusing testing, or rejecting a diagnosis of diabetes, for example “They just don’t want to admit that okay we have this disease.” These categorizations of codes were used in our analyses to quantify the types of codes developed, types of changes to code development, and timing of code development. Finally, codes were categorized as high or low prevalence. Code prevalence was defined by the number of focus group discussions in which a code was present. On average, codes were present in 7 focus group discussions; therefore, we defined high prevalence codes as those present in more than 7 focus group discussions and low prevalence codes as those present in equal to or fewer than 7 focus groups. In total, there were 27 high-prevalence codes and 23 low-prevalence codes.

To assess whether code saturation was influenced by the order in which focus groups were conducted, we randomized the order of groups and mapped hypothetical code development onto the random order. To do this, we randomized the focus groups using a random number generator, but we did not repeat the process of reviewing transcripts given the bias this would have introduced, as this process had already been done with the same transcripts in their actual order. Instead, we assumed that codes would be developed after the same number of repetitions of that issue across the focus groups. For example, in the actual code development, the code ‘cultural expectations’ was created in focus group 3, after the issue was mentioned in focus groups 1 and 2. Thus, in the random order, we assumed that the same code would likewise be developed after 3 groups in which the issue was raised. Our aim was to reflect researchers’ style of code development in the random order as in the actual order, so that we could more directly assess the effect of the order of focus groups on code development. We replicated the pattern of code development in the randomized order of groups by calculating the number of times a code was present (as indicated by the number of focus groups in which the code was applied to the data) before the focus group in which the code was created. We then used these numbers to map hypothetical code development in the randomized order of groups. We then compared hypothetical code development with that from the actual order in which focus groups were conducted.

Assessing Meaning Saturation

We followed the same process to assess meaning saturation (described below) that we used in our previous study on saturation with in-depth interview data ( Hennink et al 2016 ), with the addition of two components to reflect the use of focus group data in this study.

To assess meaning saturation, we selected 19 codes that were central to the aims of the original study on diet, exercise and diabetes and included different types of codes. These codes comprised a mix of concrete (13 codes) and conceptual codes (6 codes) and high prevalence (10 codes) and low prevalence (9 codes) codes (as defined above). This selection reflected the nature of codes developed in this study, whereby there were more concrete than conceptual codes. To assess meaning saturation, we traced these 19 codes to identify what we learned about the code in each successive focus group discussion. This involved using the coded data to search for the code in the first focus group discussion and noting what we learned about this issue from this focus group, then searching for the code in the next focus group and noting any new aspects or nuances of the code from that group, and continuing until all 10 focus groups had been reviewed. This process was repeated for all 19 codes that were traced. For each code, we noted at which focus group there were no new aspects of a code raised and no further understanding of the code, only the repetition of earlier aspects. We deemed this as the point of meaning saturation for that code. We then compared the number of focus group discussions needed to reach meaning saturation with the number needed to reach code saturation determined in our earlier analyses.

To assess whether meaning saturation is influenced by the type of code, we compared the timing of saturation for concrete and conceptual codes. Concrete codes included: ‘family time’, ‘homeopathy’, ‘exercise instructor’, ‘exercise measures’, ‘exercise gender’, ‘exercise venues’, ‘physical appearance’, ‘ingredient cost’, ‘food taste’, ‘diabetes cause’, ‘US-Indian food’, ‘exercise barriers’, and ‘exercise perception’. Conceptual codes included: ‘denial’, ‘exercise pleasure’, ‘work success’, ‘women’s responsibility’, ‘mood’, and ‘cultural expectations’. To assess whether meaning saturation is influenced by the prevalence of a code, we compared saturation by high and low prevalence codes.

To assess whether meaning saturation is influenced by the number of participants who discussed a code, we noted the number of participants contributing to the discussion of each code across all focus groups. If 4 people had discussed a code in the first focus group, 2 in the second, and 6 in the third, we determined that a total of 12 participants had discussed this code across the data. We then identified whether there was any pattern in saturation by the number of participants discussing a code. Finally, to assess how saturation is influenced by the demographic stratification of the focus groups (described earlier), we noted the age and sex composition of each group on the trajectories and identified any patterns in saturation by these strata.

Part I: Code Saturation

Code development.

Figure 1 shows the timing of code development across all focus groups in the study. The figure shows the focus group discussions in the order in which they were conducted, the number of new codes developed in each successive focus group, the type of code developed (inductive, deductive), and the demographic stratum of each focus group. A total of 50 codes were developed in the study comprising 58% inductive and 42% deductive codes. Deductive codes were developed only from focus groups 1 and 2, with only inductive codes added thereafter. The vast majority of codes (60%) were identified in the first focus group discussion, with a sharp decline in new codes after this. From the second focus group, an additional 12 codes were developed, with 84% of codes developed at this point. Focus groups 3 to 6 added 8 new codes, with only a few new codes per focus group; most of these new codes (5/8) were of low prevalence across the data. After focus group 6, no further new codes were developed.

Timing of code development and code saturation.

Note. Two deductive codes were developed in Focus Group 2. These codes were derived from questions in the discussion guide that were not probed in Focus Group 1 but were probed in Focus Group 2. FGD = focus group discussion; Y = Younger; M = Male; O = Older; F = Female.

Given that the majority of new codes (60%) were identified in the first focus group discussion, we assessed whether the order in which the focus groups were conducted influenced the pattern of new code development and code saturation, particularly given the demographic stratification of the focus groups in this study. To assess this, we compared the pattern of code development in the actual order in which focus groups were conducted with a randomized order of focus groups. Figure 2 shows the same pattern of code development in both the actual and the randomized order of focus groups, with approximately 60% of codes developed in the first focus group discussion and a strong decline in new codes identified in subsequent focus groups. We also find that both scenarios reach saturation with over 90% of codes developed at focus group 4 (94% and 92% in the actual and random order, respectively). Therefore, the order in which focus groups are conducted has little influence on the pattern of new code development or on code saturation.

Timing of code development—Actual versus randomized order of FGDs.

Note. FGD = focus group discussion.

Codebook development

We recorded codebook development by documenting the timing of changes to codes and code definitions ( Table 1 ). Most changes to code definitions occurred in focus groups 2 and 3, with no changes to the codebook occurring after focus group 6. The two most common types of code definition changes were adding examples and conceptually expanding a code definition, which consisted of adding a new dimension of the issue to the code definition. For example, the original code definition of the code ‘exercise with friends’ was “Discussion about whom to exercise with in the intervention (e.g., friends, other South Asians).” Following focus group 2, this code definition was expanded to include “social support to exercise”, and following focus group 5, it was expanded again to include the concept of “group accountability as a motivator to exercise.” While over half of the codes (58%) were inductive, most of the code definition changes (84%) were made to deductive codes, to ensure that these codes, which were derived externally from the data, effectively reflected the issue raised in the data. Some codes were refined multiple times, with over one-third of the code definition changes made to only three codes (‘exercise perception’, ‘exercise barriers’, and ‘healthy diet barriers’).

Changes to codebook

Code prevalence

To identify when more or less prevalent codes were developed, we examined code development by code prevalence and type of code. Figure 3 depicts each code as a separate bar: the location of a code on the x-axis indicates the focus group in which the code was developed, and the height of the bar shows the number of focus groups in which the code was used. For example, the first 13 bars show that these codes were all developed in the first focus group discussion and were all high prevalence codes, present in all 10 focus groups. The dashed line indicates the average number of focus group in which a code was used – about 7 focus groups. Figure 3 shows that 27 codes were of high prevalence (above the line), and 23 were of low prevalence (below the line) across all data. The majority of high-prevalence codes (81%, 22/27) were identified in the first focus group discussion, and by the third focus group, 96% (26/27) of all high-prevalence codes were identified. Thus, the vast majority of high-prevalence codes were identified in early focus group discussions. Most low-prevalence codes (65%, 15/23) were developed after the first focus group, with a clustering at focus group 2. This shows that more focus groups are needed to identify low-prevalence codes.

Timing of code development by code prevalence and type.

a Dashedline indicates average code prevalence across all data at 7.2 FGDs.

Figure 3 also shows when the different types of codes (concrete or conceptual) were developed and their prevalence across the data. The first focus group almost exclusively generated concrete codes (97%, 29/30), most of which were also of high prevalence (76%, 22/29). In contrast, half of the conceptual codes were low prevalence (50%, 3/6) and developed later, in focus groups 2, 3, and 6. Overall, codes developed in early focus groups were high prevalence concrete codes, while those developed in later focus groups were mostly low prevalence and included more conceptual codes.

Code saturation

We did not use a set threshold to determine code saturation but were guided by the results of our analyses. We determined that code saturation was reached after four focus group discussions, based on code identification (94% of codes had been identified), code prevalence (96% of high-prevalence codes were identified) and codebook stability (90% of codebook changes were made). Therefore, four focus group discussions were sufficient to identify the range of issue present in these data.

Part II: Meaning Saturation

Having established that code saturation was reached at four focus group discussions, we then explored whether four groups are also sufficient to reach meaning saturation, whereby we gain a comprehensive understanding of the issues raised. To assess meaning saturation, we traced a range of codes across all focus groups and noted what we learned about each code from successive focus groups until no more new dimensions of the code were uncovered. Meaning saturation was deemed to be reached at the last focus group discussion in which a new dimension of the code was identified. For example, the code ‘food taste’ was identified in the first focus group discussion and reached meaning saturation at the second focus group, as no more new aspects of this code were identified (after this point, there was only repetition of dimensions already identified). The code ‘work success’ was also identified in the first focus group discussion, but new aspects of the code were identified in the fourth and sixth focus group but none thereafter, so this code reached meaning saturation by focus group six. We traced 17 codes to assess meaning saturation, comprising a mix of concrete and conceptual codes, and high- and low-prevalence codes. Below we assess the influence of these factors on meaning saturation of these codes.

Figure 4 shows the results of the code tracing, indicating the focus group at which each code was first identified in data and the focus group at which it reached meaning saturation. Most codes were identified in the first focus group discussion, but they did not reach meaning saturation until later focus groups. This shows that data from multiple focus groups are needed to understand many of the issues, with successive focus groups adding different dimensions of a code until a more complete understanding of the issue is reached. Codes also reached meaning saturation at different points in the data, some requiring more data to fully understand the issue. Both concrete and conceptual codes needed data from a range of focus groups to reach meaning saturation. For example, the concrete code ‘exercise gender’ and the conceptual code ‘work success’ needed data from 6 and 7 focus groups, respectively, to capture the various perspectives on each of these issues. This shows that reaching saturation needs to go beyond code saturation (whereby codes are identified in data) towards meaning saturation to fully understand the issues raised, and capture the different dimensions, context, and nuances of the issues.

Timing of first use of codes and their meaning saturation.

* concrete code.

Figure 4 showed that codes reached meaning saturation at different points in the data. Below, we examine a range of influences on reaching meaning saturation, including the type of code (concrete or conceptual), code prevalence, demographic strata of focus groups, and the number of participants who discussed an issue across data.

We explored when different types of codes (concrete or conceptual) reached meaning saturation. Concrete codes reached meaning saturation at different points. A few concrete codes reached meaning saturation after one or two focus groups, for example the codes ‘exercise instructor’ and ‘food taste’ ( Figure 4 ). Several concrete codes clustered to reach meaning saturation at focus group 5. The remaining concrete codes reached meaning saturation later, at focus groups 6 to 11. Conceptual codes showed a more consistent pattern in reaching meaning saturation. All conceptual codes were first identified in focus group 1 or 2, but they did not reach meaning saturation until focus group 5 or later, with one code not reaching saturation (‘cultural expectations’). No conceptual codes reached meaning saturation in fewer than five focus groups, which shows that more data are needed to reach meaning saturation for conceptual codes than for some concrete codes. Overall, with the exception of three concrete codes, all other codes reached meaning saturation at focus group 5 or later.

We examined how code prevalence (high or low prevalence) influences meaning saturation. Figure 4 shows that high-prevalence codes reached meaning saturation between focus groups 4 and 10, with a clustering at focus groups 5 and 6. High-prevalence conceptual codes needed more focus groups to reach meaning saturation (6–10 focus groups) compared with high-prevalence concrete codes (4–5 focus groups). Low-prevalence codes reached meaning saturation between focus groups 2 and 8 and also showed a clustering at focus groups 5 and 6. While all low-prevalence conceptual codes reached saturation at focus group 5 or 6, low-prevalence concrete codes showed a much more mixed pattern, reaching saturation between focus groups 2 and 8. These results show that even high-prevalence codes require a range of focus groups to fully understand issues, with high-prevalence conceptual codes requiring more data than high prevalence concrete codes.

The strongest pattern in meaning saturation was found by demographic strata of the focus groups. Each focus group was stratified by age (younger/older) and sex (men/women), with two to three focus groups conducted within each stratum across the study. Figure 4 indicates the order in which focus group discussions were conducted and the characteristics of each stratum. At focus group 5, each stratum had been included once, and by focus group 10, each stratum had been included two to three times. Results show that regardless of the type of code or code prevalence (described above), meaning saturation clusters at focus group 5 once all strata have been included once. This shows that for many codes, meaning saturation is reached once the perspectives of all strata have been included, and therefore the diversity within each code has been captured. This is shown in Table 2 , which exemplifies the different dimensions of a code that are captured in each demographic stratum. For example, with the concrete code ‘exercise barriers’, the first focus group identified issues specific to this stratum of young men (e.g., priority for education over exercise and physical appearance), in addition to other issues. The second focus group adds the perspective of older women about the lack of awareness of exercise benefits beyond weight loss. The third and fourth focus group add a range of issues related to older men, such as barriers of weather and managing an exercise routine. The fifth focus group adds the perspective of young women, regarding the need to be accompanied to exercise classes and the difficulty of exercising at home. Once the perspectives of all strata are included, this code reached meaning saturation, and thereafter only repeated issues were found in later focus groups. Similarly, the conceptual code ‘mood’, which identified emotions related to diet and exercise, captures novel aspects of the code from each of the different strata before it reached meaning saturation.

Examples of code dimensions identified across demographic strata of focus group discussions

There are four codes (in Figure 4 ) that reached meaning saturation before all strata were completed. These were all concrete codes (‘family time’, ‘food taste’, ‘exercise instructor’ and ‘diabetes cause’), with little variation in the way the issue was discussed across all data. For example, the code ‘food taste’ repeatedly focused on the issue that healthy food was not tasty, with no variation across focus groups, while the code ‘exercise instructor’ highlighted that participants prioritized the experience of an instructor over their cultural background, with no further nuances of this issue across focus groups. These results show that codes that are nuanced by the characteristics of demographic strata (e.g., age or sex) reach saturation only after all strata are included, while codes that are not influenced by demographic strata will reach saturation without all strata included.

Given our results on the influence of demographic strata on meaning saturation, we assessed whether more than one focus group per stratum was needed to reach meaning saturation. Results show that some codes needed data from multiple focus groups per stratum to reach meaning saturation. These included both concrete codes (‘exercise venues’, ‘physical appearance’) and conceptual codes (‘cultural expectations’, ‘work success’). The clustering of codes reaching meaning saturation at focus group 6 also shows that a range of codes needed more than one focus group from the strata with young men and older men to reach meaning saturation. For the strata that included three focus group discussions in each – older men and older women – only one code (‘exercise gender’) identified new elements from all three focus groups with older women, suggesting this issue is nuanced only for older women. Other codes showed no new issues in the third focus group in these strata. These results show that conducting 1–2 focus groups per stratum is likely to contribute to a more comprehensive understanding of a code but suggests limited value in conducting three groups per stratum.

We also examined whether the total number of participants discussing a code influenced meaning saturation but found no clear patterns. Codes discussed by a high number of participants across data (e.g., 51) and those discussed with a low number of participants (e.g., 9) both reached meaning saturation at the same point (focus group 5). More participants discussed high-prevalence codes than low-prevalence codes and concrete codes versus conceptual codes; however, there were no patterns by meaning saturation.

Through this study, we contribute to a small but growing evidence base of empirical research on saturation. We sought to assess saturation using two approaches - code saturation and meaning saturation - and to develop parameters of saturation to estimate and justify sample sizes for focus group research in advance of data collection.

Our results show that code saturation was reached at four focus group discussions, whereby 94% of all codes and 96% of high-prevalence codes had been identified, and the codebook had stabilized. The first focus group generated 60% of all new codes with a sharp decline thereafter, regardless of the order in which focus groups were conducted. Most codes developed in early focus groups were high-prevalence, concrete codes. Comparing these results with our earlier study on saturation in in-depth interviews ( Hennink et al 2016 ), we also found the first in-depth interview generated the majority of new codes (53%), most of which were also concrete and high-prevalence codes. While the first focus group generated more new codes than the first in-depth interview, it is not remarkably higher considering issues are generated by a group of participants. These results also reflect the findings of other studies examining saturation in focus group data, whereby code saturation was reached at 5 focus groups ( Coenen et al 2012 ) and between 3 to 6 focus groups ( Guest et al 2016 ). These collective findings provide important evidence that relatively few focus groups are needed to generate a majority of new issues in a study. This contradicts general guidelines provided in academic literature (albeit not based on empirical research) recommending much higher numbers of focus groups in a study (e.g., 10, 20, or 40 focus groups). However, it is important to remember that code saturation identifies the presence of issues in data, in particular high-prevalence concrete issues, but may not provide a full understanding of all issues, their diversity, or nuances. This goal may be suitable for some research objectives, particularly for designing research instruments or interventions; however, these limitations of code saturation should be borne in mind if using this strategy.

We found that reaching meaning saturation requires more data than code saturation. While four focus groups were sufficient to identify the majority of issues across the data, more data were needed to fully understand these issues. Our results showed that even issues identified in the first focus group discussion needed more data to fully understand the issue, regardless of the type of code (concrete/conceptual; high/low prevalence). In addition, codes reached meaning saturation at different points in the data; some codes required much more data than others to reach meaning saturation. Even low-prevalence and conceptual issues contributed to building a comprehensive understanding of a phenomenon; therefore, the prevalence of issues in data does not indicate their significance in understanding the study phenomenon. As indicated by Morse (1995 , p148), “it is often the infrequent gem that puts other data into perspective, that becomes the central key to understanding the data…it is the implicit that is interesting”. Reaching meaning saturation thus relates to the “informational power” (Malterud et al 2015) of the sample to provide depth of understanding of the issues. It goes beyond identifying the presence of issues and moves towards gaining “conceptual depth” to capture the range, complexity, subtlety, resonance, and thereby the validity of issues in data ( Nelson 2016 ). Identifying a sample size adequate to meet these characteristics is critical to maximize the benefits of conducting qualitative research.

The most consistent influence on meaning saturation was the demographic strata of the focus groups. Meaning saturation clustered at focus group five for most concrete codes – this represents the point at which at least one focus group from each demographic stratum was included. This indicates that once the perspectives of each demographic stratum have been captured, meaning saturation was reached on most codes. This finding is compelling because it shows that codes that are nuanced by the characteristics of demographic strata will reach saturation only after all strata are included, while codes not influenced by demographic strata will reach saturation without all strata included, as these issues have less diversity by these characteristics. In our data, the topics diet and exercise are highly nuanced by both sex and age for the South Asian study population; therefore, we continued to identify new insights across codes with each demographic stratum until all strata were included, with few new insights thereafter. Most conceptual codes needed more data, beyond one focus group per stratum to reach meaning saturation.

Many researchers stratify focus groups by demographic characteristics precisely because they anticipate different nuances to emerge from the various strata and to enable analytic comparisons to distinguish patterns in data. The most useful strata to use are often guided by research literature and built into the study design. Overall, it is not the number of groups per se that determines meaning saturation but the point at which all strata are included in the study – in our study this was at five focus groups, but for other studies this point may be different. A common guideline for focus group research is to conduct at least two focus groups for each demographic stratum in the study ( Krueger and Casey 2015 ; Barbour 2007 ; Fern 2001 ; Greenbaum 2000 ; Morgan 1997 ). Our results support conducting two groups per stratum to provide a more comprehensive understanding of issues in particular to fully capture nuances of conceptual codes. However, we found little additional benefit in conducting more than two groups per stratum.

These results have important implications for estimating sample sizes a priori for focus group studies. Based on our findings, we recommend using both the number of strata and number of groups per stratum as key criteria to identify an adequate sample size to reach meaning saturation. For example, researchers doing a focus group study stratified by one characteristic (e.g., sex) would need to conduct two groups to include both strata but should ideally conduct two groups for each of these strata - thereby making a sample size of 4 focus groups. Researchers doing a study using two strata (e.g., sex and age) would need to conduct four groups to include all strata (e.g. younger women, older women, younger men, and older men) and ideally should conduct two groups for each of these strata, for a total sample size of eight focus groups. While this strategy may not be new to seasoned qualitative researchers, our study provides the empirical evidence that was previously lacking to support this approach and gives clear justification for why more groups per stratum are not necessarily better. For focus group studies where groups are not stratified by demographic characteristics, authors of other empirical studies have provided guidance on reaching saturation - Guest et al (2016) show that a homogenous study population where focus groups are not stratified can reach saturation in three to six focus groups ( Guest et al 2016 ), and even with a more diverse study population, saturation may be reached at five focus groups ( Coenen et al 2012 ).

We propose that an adequate sample size to reach saturation in focus group research depends on a range of parameters and is likely to differ from one study to the next. Therefore, providing universal sample size recommendations for focus groups studies is not useful. Instead we present a range of parameters based on our study findings that influence saturation in focus groups, which can be used to estimate saturation across different studies ( Table 3 ). These parameters include the study purpose, code characteristics, group composition, and desired type and degree of saturation. Each parameter needs to be considered individually, but the estimated sample size is determined by the combination of all parameters rather than by any single parameter alone. For example, one parameter may suggest a smaller sample size, but collectively they may indicate a larger sample is needed. Therefore, researchers need to assess each study by its specific characteristics and how these may influence saturation to determine an appropriate sample size. Although saturation is ultimately determined during data collection, these parameters provide guidance on identifying and justifying a sample size a priori, such as for a research proposal, but they can equally be used to justify the basis on which saturation was assessed or achieved in a completed study. Often the justifications for sample sizes or reaching saturation are absent in published qualitative research, perhaps because there is little empirical guidance on how to do this. Sample size estimates also need to remain flexible to allow the inductive process to be used during data collection; often this is achieved by identifying a range rather a fixed number when a sample size is proposed in advance (e.g., 4–6 focus groups).

Parameters influencing saturation and sample size for focus group discussions

It is also important to remain pragmatic on the degree of saturation sought (e.g., 80% or 90%). While it is near impossible to reach total saturation, since there is always the potential to discover new things in data, this is also not the objective of saturation ( Corbin and Straus 2008 ; Saunders et al 2017 ). It is not reaching a particular benchmark that is critical but reaching a point where it is determined that new discoveries do not add further insights, thus reaching a point of ‘diminishing returns’ in terms of developing a sufficiently robust understanding of the phenomenon ( Mason 2010 ). While this assessment can only be made during data review, our study provides useful guidelines on when this may occur in focus group data.

Study Limitations

To assess the effect of focus group order on saturation, we used a hypothetical randomization of focus group order, rather than repeating the process of code development using the randomized order of focus group discussions. While the benefits of this approach outweighed the risk of bias had the same researchers repeated the code development process, we could have recruited another group of researchers to conduct this task. Additionally, there is a chance that our findings are influenced by coding preferences and practices of the researchers involved. For example, researcher’s coding style (e.g., lumper vs. splitter) could affect the number and scope of codes developed, and other researchers might have had slightly different findings regarding timing of saturation had they taken a very different approach.

With this study, we contribute empirical research to identify influences on saturation in focus group research. We examined two approaches to assessing saturation and use our results to develop parameters of saturation that may be used to determine effective sample sizes for focus group studies in advance of data collection. Our results show that reaching code saturation captures the breadth of issues and requires few focus groups, while achieving meaning saturation requires more focus groups for greater depth and understanding of these issues. We also identify the strong influence of demographic strata of focus groups on saturation and sample size. If saturation continues to be hailed as the criterion for rigor in determining an adequate sample size in qualitative research, still further research is needed to examine the nature of saturation in different types of data, data collection methods, and research approaches.

The authors declare that there is no conflict of interest.

i This is sometimes called ‘information redundancy’.

Contributor Information

Monique M. Hennink, Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA.

Bonnie N. Kaiser, Department of Anthropology, University of California, San Diego (previous) Duke University (current)

Mary Beth Weber, Hubert Department of Global Health, Rollins School of Public Health, Emory University.

• Barbour R (2007). Doing Focus Groups. The SAGE Qualitative Research Kit . Sage Publications: London. [ Google Scholar ]
• Birks J, and Mills J (2011) Grounded Theory: A Practical Guide . Sage Publications:London. [ Google Scholar ]
• Bowen G (2008). Naturalistic inquiry and the saturation concept: A research note . Qualitative Research , 8 , 137–152. doi: 10.1177/1468794107085301. [ CrossRef ] [ Google Scholar ]
• Bryman A (2012). Social Research Methods . Fourth Edition, Oxford University Press, Oxford UK. [ Google Scholar ]
• Carlsen B, Glenton C (2011). What about N? A methodological study of sample-size reporting in focus group studies . BMC Medical Research Methodology , 11 , Article 26. doi: 10.1186/1471-2288-11-26. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Charmaz K (2014). Conducting Grounded Theory. A Practical Guide through Qualitative Analysis . Second Edition. London; SAGE. [ Google Scholar ]
• Coenen M, Coenen T, Stamm A, Stucki G, and Cieza A (2012). Individual interviews and focus groups with patients with rheumatoid arthritis: A comparison of two qualitative methods . Quality of Life Research , 21 :359–70. doi: 10.1007/s11136-011-9943-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corbin J and Strauss A (2008). Basics of Qualitative Research: Techniques and Procedures for developing grounded theory . Sage: Thousand Oaks, CA. [ Google Scholar ]
• Corbin J and Strauss A (2015). Basics of Qualitative Research: Techniques and Procedures for developing grounded theory . Fourth Edition. Sage: Thousand Oaks, CA. [ Google Scholar ]
• Crandall JP, Knowler WC, Kahn SE, et al. (2008). The prevention of type 2 diabetes . Nat Clin PractEndocrinolMetab . 2008: 4 ( 7 ):382–393. doi: 10.1038/ncpendmet0843. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fern E (2001). Advancing Focus Group Research . Sage: Thousand Oaks: CA [ Google Scholar ]
• Francis J, Johnson M, Robertson C, Glidewell L, Entwistle V, Eccles M, Grimshaw J (2010). What is an adequate sample size? Operationalising data saturation for theory-based interview studies . Psychology & Health , 25 , 1229–1245. doi: 10.1080/08870440903194015. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glaser B, Strauss A (1967). The discovery of grounded theory: Strategies for qualitative research . Chicago: Aldine. [ Google Scholar ]
• Greenbaum T (2000). Moderating Focus Groups A Practical Guide for Group Facilitation . Sage: Thousand Oaks: CA [ Google Scholar ]
• Guest G, Bunce A, Johnson L (2006). How many interviews are enough? An experiment with data saturation and variability . Field Methods , 18 , 59–82. doi: 10.1177/1525822X05279903. [ CrossRef ] [ Google Scholar ]
• Guest G, Namey E, McKenna K, (2016). How many focus groups are enough? Building an Evidence Base for Non-Probability sample sizes . Field Methods , Vol 29 ( 1 ), 3–22. doi: 10.1177/1525822X16639015. [ CrossRef ] [ Google Scholar ]
• Gujral UP, Pradeepa R, Weber MB, Narayan KM, Mohan V. (2013). Type 2 diabetes in South Asians: similarities and differences with white Caucasian and other populations . Ann N Y Acad Sci . 2013: 1281 :51–63. doi: 10.1111/j.1749-6632.2012.06838.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hennink M, Kaiser B, and Marconi V (2016). Code saturation versus meaning saturation: How many interviews are enough? Qualitative Health Research , Vol 27 ( 4 ). doi: 10.1177/1049732316665344. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kerr C, Nixon A, Wild D (2010). Assessing and demonstrating data saturation in qualitative inquiry supporting patient-reported outcomes research . Expert Review of Pharmacoeconomics & Outcomes Research , 10 , 269–281. doi: 10.1586/erp.10.30. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Krueger R and Casey M (2015) Focus Groups: A Practical Guide for Applied Research , Fifth edition. Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Malterud K, Siersma V, Guassora A (2016). Sample size in qualitative interview studies: Guided by information power . Qualitative Health Research . Vol 26 ( 13 ), 1753–1760. doi: 10.1177/1049732315617444. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mason M (2010). Sample size and saturation in PhD studies using qualitative interviews . Forum for Qualitative Health Research , 11 ( 3 ), art 8. doi: 10.17169/fqs-11.3.1428. [ CrossRef ] [ Google Scholar ]
• Morgan D (1997) Focus Groups as Qualitative Research, Second edition, Qualitative Research Methods Series , vol. 16 Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Morse J (1995). The significance of saturation [Editorial] . Qualitative Health Research , 5 , 147–149. doi: 10.1177/104973239500500201. [ CrossRef ] [ Google Scholar ]
• Morse J (2000). Determining sample size [Editorial] . Qualitative Health Research , 10 , 3–5. doi: 10.1177/104973200129118183. [ CrossRef ] [ Google Scholar ]
• Morse J (2015). Data were saturated … [Editorial] . Qualitative Health Research , 25 , 587–588. doi: 10.1177/1049732315576699. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nelson J (2016). Using conceptual depth criteria: addressing the challenge of reaching saturation in qualitative research . Qualitative Research . 17 ( 5 ). doi: 10.1177/1468794116679873. [ CrossRef ] [ Google Scholar ]
• Patton M (2015) Qualitative Research and Evaluation Methods , 4rd edition. Thousand Oaks, CA: Sage Publications. [ Google Scholar ]
• Saunders B, Sim J, Kingstone T, Baker S, Waterfield J, Bartlam B, Burroughs H and Jinks C (2017). Saturation in qualitative research: exploring its conceptualization and operationalization . Qual Quant , doi: 10.1007/s11135-017-0574-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Saturation in qualitative research: exploring its conceptualization and operationalization

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• Published: 14 September 2017
• Volume 52 , pages 1893–1907, ( 2018 )

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• Benjamin Saunders 1 ,
• Julius Sim   ORCID: orcid.org/0000-0002-1816-1676 1 ,
• Tom Kingstone 1 ,
• Shula Baker 1 ,
• Jackie Waterfield 2 ,
• Bernadette Bartlam 1 ,
• Heather Burroughs 1 &
• Clare Jinks 1  

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Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation—as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

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1 Introduction

In broad terms, saturation is used in qualitative research as a criterion for discontinuing data collection and/or analysis. Footnote 1 Its origins lie in grounded theory (Glaser and Strauss 1967 ), but in one form or another it now commands acceptance across a range of approaches to qualitative research. Indeed, saturation is often proposed as an essential methodological element within such work. Fusch and Ness ( 2015 : p. 1408) claim categorically that ‘failure to reach saturation has an impact on the quality of the research conducted’; Footnote 2 Morse ( 2015 : p. 587) notes that saturation is ‘the most frequently touted guarantee of qualitative rigor offered by authors’; and Guest et al. ( 2006 : p. 60) refer to it as having become ‘the gold standard by which purposive sample sizes are determined in health science research.’ A number of authors refer to saturation as a ‘rule’ (Denny 2009 ; Sparkes et al. 2011 ), or an ‘edict’ (Morse 1995 ), of qualitative research, and it features in a number of generic quality criteria for qualitative methods (Leininger 1994 ; Morse et al. 2002 ).

However, despite having apparently attained something of the status of orthodoxy, saturation is defined within the literature in varying ways—or is sometimes undefined—and raises a number of problematic conceptual and methodological issues (Dey 1999 ; Bowen 2008 ; O’Reilly and Parker 2013 ). Drawing on a number of examples in the literature, this paper seeks to explore some of these issues in relation to three core questions:

‘What?’—in what way(s) is saturation defined?

‘Where and why?’—in what types of qualitative research, and for what purpose, should saturation be sought?

‘When and how?’—at what stage in the research is saturation sought, and how can we assess if it has been achieved?

In addressing these questions, we will explore the implications of different models of saturation—and the theoretical and methodological assumptions that underpin them—for the varying purposes saturation may serve across different qualitative approaches. In doing so, the paper will contribute to the small but growing literature that has critically examined the concept of saturation (e.g. Bowen 2008 ; O’Reilly and Parker 2013 ; Walker 2012 ; Morse 2015 ; Nelson 2016 ), aiming to extend the discussion around its conceptualization and use. We will argue not only for greater transparency in the reporting of saturation, as others have done (Bowen 2008 ; Francis et al. 2010 ), but also for a more thorough consideration on the part of qualitative researchers regarding how saturation relates to the research question(s) they are addressing, in addition to the theoretical and analytical approach they have adopted, with due recognition of potential inconsistencies and contradictions in its use.

2 ‘What?’—in what way(s) is saturation defined?

In their original treatise on grounded theory, Glaser and Strauss ( 1967 : p. 61) defined saturation in these terms:

The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation . Saturation means that no additional data are being found whereby the sociologist can develop properties of the category. As he sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. He goes out of his way to look for groups that stretch diversity of data as far as possible, just to make certain that saturation is based on the widest possible range of data on the category.

Here, the decision to be made relates to further sampling, and the determinant of adequate sampling has to do with the degree of development of a theoretical category in the process of analysis. Saturation is therefore closely related to the notion of theoretical sampling—the idea that sampling is guided by ‘the necessary similarities and contrasts required by the emerging theory’ (Dey 1999 : p. 30)—and causes the researcher to ‘combine sampling, data collection and data analysis, rather than treating them as separate stages in a linear process’ (Bryman 2012 : p. 18).

Also writing from a grounded theory standpoint, Urquhart ( 2013 : p. 194) defines saturation as: ‘the point in coding when you find that no new codes occur in the data. There are mounting instances of the same codes, but no new ones’, whilst Given ( 2016 : p. 135) considers saturation as the point at which ‘additional data do not lead to any new emergent themes’. A similar position regarding the (non)emergence of new codes or themes has been taken by others (e.g. Birks and Mills 2015 ; Olshansky 2015 ). Footnote 3 These definitions show a change of emphasis, and suggest a second model of saturation. Whilst the focus remains at the level of analysis, the decision to be made appears to relate to the emergence of new codes or themes, rather than the degree of development of those already identified. Moreover, Urqhart ( 2013 ) and Birks and Mills ( 2015 ) relate saturation primarily to the termination of analysis, rather than to the collection of new data.

According to Starks and Trinidad ( 2007 : p. 1375), however, theoretical saturation occurs ‘when the complete range of constructs that make up the theory is fully represented by the data’. Whilst not wholly explicit, this definition suggests a third model of saturation with a different directional logic: not ‘given the data, do we have analytical or theoretical adequacy?’, but ‘given the theory, do we have sufficient data to illustrate it?’ Footnote 4

If we move outside the grounded theory literature, Footnote 5 a fourth perspective becomes apparent in which there are references to data saturation, rather than theoretical saturation (e.g. Fusch and Ness 2015 ). Footnote 6 This view of saturation seems to centre on the question of how much data (usually number of interviews) is needed until nothing new is apparent, or what Sandelowski ( 2008 : p. 875) calls ‘informational redundancy’ (e.g. Francis et al. 2010 ; Guest et al. 2006 ). Grady ( 1998 : p. 26) provides a similar description of data saturation as the point at which:

New data tend to be redundant of data already collected. In interviews, when the researcher begins to hear the same comments again and again, data saturation is being reached… It is then time to stop collecting information and to start analysing what has been collected.

Whilst several others have defined data saturation in a similar way (e.g. Hill et al. 2014 : p. 2; Middlemiss et al. 2015 ; Jackson et al. 2015 ), Legard et al. ( 2003 ) seem to adopt a narrower, more individual-oriented perspective on data saturation, whereby saturation operates not at the level of the dataset as a whole, but in relation to the data provided by an individual participant; i.e. it is achieved at a particular point within a specific interview:

Probing needs to continue until the researcher feels they have reached saturation, a full understanding of the participant’s perspective (Legard et al. 2003 : p. 152).

From this perspective, the researcher’s response to the data—through which decisions are made about whether or not any new ‘information’ is being generated—is not necessarily perceived as forming part of the analysis itself. Thus, in this model, the process of saturation is located principally at the level of data collection and is thereby separated from a fuller process of data analysis, and hence from theory.

Four different models of saturation seem therefore to exist (Table  1 ). The first of these, rooted in traditional grounded theory, uses the development of categories and the emerging theory in the analysis process as the criterion for additional data collection, driven by the notion of theoretical sampling; using a term in common use, but with a more specific definitional focus, this model could thus be labelled as theoretical saturation . The second model takes a similar approach, but saturation focuses on the identification of new codes or themes, and is based on the number of such codes or themes rather than the completeness of existing theoretical categories. This can be termed inductive thematic saturation . In this model, saturation appears confined to the level of analysis; its implication for data collection is at best implicit. In the third model, a reversal of the preceding logic is suggested, whereby data is collected so as to exemplify theory, at the level of lower-order codes or themes, rather than to develop or refine theory. This model can be termed a priori thematic saturation , as it points to the idea of pre-determined theoretical categories and leads us away from the inductive logic characteristic of grounded theory. Finally, the fourth model—which, again aligning with the term already in common use, we will refer to as data saturation —sees saturation as a matter of identifying redundancy in the data, with no necessary reference to the theory linked to these data; saturation appears to be distinct from formal data analysis.

2.1 ‘Hybrid’ forms of saturation

Some authors appear to espouse interpretations of saturation that combine two or more of the models defined above, making its conceptualization less distinct. For example, Goulding ( 2005 ) suggests that both data and theory should be saturated within grounded theory, and Drisko ( 1997 : p. 192) defines saturation in terms of ‘the comprehensiveness of both the data collection and analysis’. Similarly, Morse’s view of saturation seems to embody elements of both theoretical and data saturation. She links saturation with the idea of replication, in a way that suggests a process of data saturation:

However, when the domain has been fully sampled – when all data have been collected – then replication of data occurs and, with this replication… the signal of saturation (Morse 1995 : p. 148).

Morse notes elsewhere that she is able to tell when her students have achieved saturation, as they begin to talk about the data in more generalized terms and ‘can readily supply examples when asked. These students know their data’ (Morse 2015 : p. 588). This too suggests a form of data saturation. However, Morse also proposes that saturation is lacking when ‘there are too few examples in each category to identify the characteristics of concepts, and to develop theory’ (Morse 2015 : p. 588). This perspective seems to be located firmly in the idea of theory development (as other parts of the quoted papers by Morse make clear), though a heavy emphasis is placed at the level of the data and the way in which the data exemplify theory, thereby seeming to evoke both data and theoretical saturation.

Hennink et al. ( 2017 ) go further, appearing to combine elements of all four models of saturation. They firstly identify ‘code saturation’, the point at which ‘no additional issues are identified and the codebook begins to stabilize’ ( 2017 : p. 4), which seems to combine elements of both inductive thematic saturation and data saturation. However, within this approach saturation is discussed as relating not only to codes developed inductively, but also to a priori codes, which echoes the third model: a priori thematic saturation. They go on to distinguish ‘code saturation’ from ‘meaning saturation’; in the latter, the analyst attempts to ‘fully understand conceptual codes or the conceptual dimensions of… concrete codes’ ( 2017 : p. 14). This focus on saturating the dimensions of codes seems more akin to theoretical saturation; however, their analysis remains at the level of codes, rather than theoretical categories developed from these codes, and Hennink et al. explicitly position their approach outside grounded theory methods.

3 ‘Where and why?’—in what types of qualitative research, and for what purpose, should saturation be sought?

Morse ( 2015 : p. 587) takes the view that saturation is ‘present in all qualitative research’ and as previously noted, it is commonly considered as the ‘gold standard’ for determining sample size in qualitative research, with little distinction between different types of qualitative research. We question this perspective, and would instead argue—as is suggested by the different models of saturation considered in the previous section—that saturation has differing relevance, and a different meaning, depending on the role of theory, a viewpoint somewhat supported by other commentators who have questioned its application across the spectrum of qualitative methods (Walker 2012 ; O’Reilly and Parker 2013 ; van Manen et al. 2016 ).

In a largely deductive approach (i.e. one that relies wholly or predominantly on applying pre-identified codes, themes or other analytical categories to the data, rather than allowing these to emerge inductively) saturation may refer to the extent to which pre-determined codes or themes are adequately represented in the data—rather like the idea of the categories being sufficiently replete with instances, or ‘examples’, of data, as suggested in the a priori thematic saturation model outlined above. Thus, in their attempt to establish an adequate sample size for saturation, Francis et al. ( 2010 ) refer explicitly to research in which conceptual categories have been pre-established through existing theory, and it is significant in this respect that they link saturation with the notion of content validity. In contrast, within a more inductive approach (e.g. the inductive thematic saturation and theoretical saturation models outlined above), saturation suggests the extent to which ‘new’ codes or themes are identified within the data, and/or the extent to which new theoretical insights are gained from the data via this process.

In both the deductive and the inductive approach, we can make sense of the role of saturation, however much it differs in each case, because the underlying approach to analysis is essentially thematic, and usually occurs in the context of interview or focus group studies involving a number of informants. It is less straightforward to identify a role for saturation in qualitative approaches that are based on a biographical or narrative approach to analysis, or that, more generally, include a specific focus on accounts of individual informants (e.g. interpretative phenomenological analysis). In such studies, analysis tends to focus more on strands within individual accounts rather than on analytical themes ; these strands are essentially continuous, whereas themes are essentially recurrent. Accordingly, Marshall and Long ( 2010 ) suggest that saturation was not appropriate in their study of maternal coping processes, based on narrative methods. Elsewhere, however, a less straightforward picture emerges. Hawkins and Abrams ( 2007 ) utilized saturation in the context of a study based on life-history interviews with 39 formerly homeless mentally ill men and women. The authors state: ‘Of the 39 participants, six did not complete a second interview because they were unavailable, impaired, or the research team felt the first interview had achieved saturation’ (p. 2035), suggesting that judgments of saturation were made within each participant’s account. Power et al. ( 2015 ) adopted a story-telling approach to women’s experience of post-partum hospitalization, and recruitment continued until data saturation, which was established through ‘the repetition of responses’ (p. 372). Analysis was thematic, and it is not clear whether saturation was determined in relation to themes across participants’ stories, or within individual stories. Similarly, in a study of osteoarthritis in footballers, based on interpretative phenomenological analysis, Turner et al. ( 2002 ) employed saturation, which was defined both in terms of the emergence of themes from the analysis and a ‘consensus across views expressed’ (p. 298), which suggests that, notwithstanding the interpretive phenomenological analysis perspective adopted, saturation was sought more across than within cases. Hale et al. ( 2007 : p. 91) argue, however, that saturation is not normally an aim in interpretative phenomenological analysis, owing to the concern to obtain ‘full and rich personal accounts’, which highlights the particular analytical focus within individual accounts in this approach, and van Manen dissociates saturation from phenomenological research more generally (van Manen et al. 2016 ).

Considering the various types of research in which saturation might feature helps to clarify the purposes it is intended to fulfil. When used in a deductive approach to analysis, saturation serves to demonstrate the extent to which the data instantiate previously determined conceptual categories, whereas in more inductive approaches, and grounded theory in particular, it says something about the adequacy of sampling in relation to theory development (although we have seen that there are differing accounts of how specifically this should be achieved). In narrative research, a role for saturation is harder to discern. Rather than the sufficient development of theory, it might be seen to indicate the ‘completeness’ of a biographical account. However, one could question whether the point at which a participant’s story is interpreted as being ‘complete’—having presumably conveyed everything seen to be relevant to the focus of the study—is, in fact, usefully described by the concept of saturation, given the distance that this moves us away from the operationalization of saturation in broadly thematic approaches. This might, furthermore, lead us to ask whether there is the risk of saturation losing its coherence and utility if its potential conceptualization and uses are stretched too widely.

The same issue is relevant with regard to a number of other, less obvious, purposes that have been proposed for saturation. For example, it has been claimed to demonstrate the trustworthiness of coding (Damschroder et al. 2007 )—but as saturation will be a direct and automatic consequence of one’s coding decisions, it is not clear how it can be an independent measure of their quality. Dubé et al. ( 2016 ) suggest that saturation says something about (though not conclusively) the ability to extrapolate findings, and Boddy ( 2016 : p. 428) claims that ‘once saturation is reached, the results must be capable of some degree of generalisation’; this seems to move us away from the notion of the theoretical adequacy of an analysis, and the explanatory scope of a theory, toward a much more empirical sense of generalizability. The use of saturation in these two cases could perhaps indicate a degree of confusion in some studies about the meaning of saturation and its purpose, even when taking into account the differing models of saturation outlined earlier. Therefore, we would suggest that for saturation to be conceptually meaningful and practically useful there should be some limit to the purposes to which it can be applied.

4 ‘When and how?’—at what stage in the research is saturation sought, and how can we assess if it has been achieved?

4.1 perspectives taken on saturation.

The perspective taken on what is meant by saturation within a given study will have implications for when it will be sought. Taking the fourth model of saturation identified earlier—the data saturation approach, as based on the notion of informational redundancy—it is clear that saturation can be identified at an early stage in the process, as from this perspective saturation is often seen as separate from, and preceding, formal analysis. Decisions about when further data collection is unnecessary are commonly based on the researcher’s sense of what they are hearing within interviews, and this decision can therefore be made prior to coding and category development. In a focus group study of HIV perceptions in Ghana, Ganle ( 2016 ) used the notion of saturation to determine when each focus group discussion should terminate. Such a decision would seem, however, to relate to only a very preliminary stage of analysis and is likely to be driven by only a rudimentary sense of any emergent theory. A similar point can be made in relation to Hancock el al.’s ( 2016 ) study of male nurses’ views on selecting a nursing speciality. They talk of logging each instance in which their focus group participants ‘discussed a theme’, with saturation then judged in relation to the number of times themes were discussed. Though not elaborated upon, this appears to imply a very narrow definition of a theme as something that can be somehow ‘observed’ during the course of a focus group. However, interpretations at this stage regarding what might constitute a theme, before even beginning to consider whether identified themes are saturated, will be superficial at best. Moreover, conclusions reached at this stage may not be particularly informative as regards subsequent theory development—pieces of data that appear to be very similar when first considered may be found to exemplify different theoretical constructs on detailed analysis, and correspondingly, data that are empirically dissimilar may turn out to have much in common theoretically. Judgments at this stage will also relate to a framework of themes and categories that is theoretically immature, and that may be subject to considerable modification; for example, the changes that may occur during the successive stages of open, selective and theoretical coding in grounded theory (Glaser 1978 ).

With regard to the second model identified, inductive thematic saturation, the fact that the focus is more explicitly on reaching saturation at the level of analysis—i.e. in relation to the (non-)emergence of new codes or themes—might suggest it will be achieved at a later stage than in data saturation approaches (notwithstanding the concurrent nature of data-collection and analysis in many qualitative approaches). However, focusing on the emergence or otherwise of codes rather than on their theoretical development still points us towards saturation being achieved at a relatively early stage. Hennink et al. ( 2017 ) highlight this in a study on patient retention in HIV care, in which they found that saturation of codes was achieved at an earlier point than saturation of the ‘dimensions, nuances, or insights’ related to codes. Hennink et al. argue that an approach to saturation relying only on the number of codes ‘misses the point of saturation’ ( 2017 : p. 15) owing to a lack of understanding of the ‘meaning’ of these codes.

In contrast to data saturation and inductive thematic saturation, the first model of saturation considered, theoretical saturation—as based on the grounded theory notion of determining when the properties of theoretical categories are adequately developed—indicates that the process of analysis is at a more advanced stage and at a higher level of theoretical generality. Accordingly, Zhao and Davey ( 2015 : p. 1178) refer to a form of saturation determined by ‘theoretical completeness’ and ceased sampling ‘when dimensions and gaps of each category of the grounded theory had been explicated,’ and Bowen ( 2008 ) gives a detailed account of how evidence of saturation emerged at the level of thematic categories and the broader process of theory construction.

4.2 Saturation as event or process

A key issue underlying the identification of saturation is the extent to which it is viewed as an event or a process. Commonly, saturation is referred to as a ‘point’ (e.g. Otmar et al. 2011 ; Jassim and Whitford 2014 ; Kazley et al. 2015 ), suggesting that it should be thought of as a discrete event that can be recognized as such by the analyst. Strauss and Corbin ( 1998 : p. 136), however, talk about saturation as a ‘matter of degree’, arguing that there will always be the potential for ‘the “new” to emerge’. They suggest that saturation should be more concerned with reaching the point where further data collection becomes ‘counter-productive’, and where the ‘new’ does not necessarily add anything to the overall story or theory. Mason ( 2010 ) makes a similar argument, talking of the point at which there are ‘diminishing returns’ from further data-collection, and a number of researchers seem to take this more incremental approach to saturation. Aiken et al. ( 2015 : p. 154), for example, refer in their interview study of unintended pregnancy to being ‘confident of having achieved or at least closely approached thematic saturation.’ Nelson ( 2016 ), echoing Dey’s ( 1999 ) earlier view, argues that the term ‘saturation’ is itself problematic, as it intuitively lends itself to thinking in terms of a fixed point and a sense of ‘completeness’. He thus argues that ‘conceptual depth’ may be a more appropriate term—at least from a grounded theory perspective—whereby the researcher considers whether sufficient depth of understanding has been achieved in relation to emergent theoretical categories.

On this incremental reading of saturation, the analysis does not suddenly become ‘rich’ or ‘insightful’ after that one additional interview, but presumably becomes rich er or more insightful. The question will then be ‘how much saturation is enough?’, rather than ‘has saturation occurred?’ Footnote 7 This is a less straightforward question, but one that much better highlights the fact that this can only be a matter of the analyst’s decision—saturation is an ongoing, cumulative judgment that one makes, and perhaps never completes, Footnote 8 rather than something that can be pinpointed at a specific juncture.

4.3 Uncertainty and equivocation

A desire to identify a specific point in time at which saturation is achieved seems often to give rise to a degree of uncertainty or equivocation. In a number of studies, saturation is claimed, but further data collection takes place in an apparent attempt to ‘confirm’ (Jassim and Whitford 2014 : p. 191; Forsberg et al. 2000 : p. 328) or ‘validate’ (Vandecasteele et al. 2015 : p. 2789) this claim; for example:

After the 10th interview, there were no new themes generated from the interviews. Therefore, it was deemed that the data collection had reached a saturation point. We continued data collection for two more interviews to ensure and confirm that there are no new themes emerging (Jassim and Whitford ( 2014 : pp. 190–191).

Furthermore, a reluctance to rely on evidence of saturation sometimes indicates that saturation is being used in at best an unclear, or at worst an inconsistent or incoherent, fashion. For example, Hill et al. ( 2014 : p. 2), whilst espousing the principle of saturation, seem not fully to trust it:

Saturation was monitored continuously throughout recruitment. For completeness we chose to fully recruit to all participant groups to reduce the chance of missed themes.

Similarly, Jackson et al. ( 2000 : p. 1406) claim that saturation had been established, but then appear to retreat somewhat from this conclusion:

Following analysis of eight sets of data, data saturation was established… however, two additional participants were recruited to ensure data saturation was achieved.

Constantinou et al. ( 2017 ) propose that, given the potential for uncertainty about the point at which saturation is reached, attention should focus more on providing evidence that saturation has been reached, than on concerns about the point at which this occurred. Thus, rather curiously, they propose that it ‘does not hurt to include all interviews from the initial sampling’ ( 2017 : p. 13). This view is inherently problematic, however, as not only does it imply that saturation is a retrospective consideration following the completion of data collection, rather than as guiding ongoing sampling decisions, but one could also argue that saturation loses its relevance if all data are included regardless of whether or not they contribute further insights or add to conceptual understanding. This approach appears to indicate a preoccupation with having enough data to show evidence of saturation, i.e. not too few interviews, rather than saturation aiding decisions about the adequacy of the sample.

Whilst the above suggests ambivalence towards assessing the point at which saturation is achieved, others report having made the conscious decision to continue sampling beyond saturation, appearing to seek additional objective evidence to bolster their sampling decisions. For instance, in investigating staff and patient views on a stroke unit, Tutton et al. ( 2012 : p. 2063) talk of how, despite having achieved saturation, ‘increased observation may have increased the degree of immersion in the lives of those on the unit’, whilst Naegeli et al. ( 2013 : p. 3) look to gain ‘more in-depth understanding… beyond the saturation point’. Similar points are made by Kennedy et al. ( 2012 : p. 859), who talk of looking for ‘novel aspects’ after the achievement of saturation, and Poletti et al. ( 2007 : p. 511), who propose the need to ‘fill gaps in the data’ following saturation. These examples suggest a view that there is something of theoretical importance that is not captured by saturation, though it is unclear from the explanations given as to exactly what this is. Footnote 9

Another indication of an ambivalent view taken on saturation is suggested by Mason’s ( 2010 ) observation that sample sizes in studies based on interviews are commonly multiples of ten. This suggests that, in practice, rules of thumb or other a priori guidelines are commonly used in preference to an adaptive approach such as saturation. Quite frequently, studies that adopt the criterion of saturation propose at the same time a prior sample size (e.g. McNulty et al. 2015 ; Long-Sutehall et al. 2011 ). In a similar way, Niccolai et al. ( 2016 ) sought saturation during their analysis, but also state (p. 843) that:

An a priori sample size of 30 to 40 was selected based on recommendations for qualitative studies of this nature… and the anticipated complexity and desired level of depth for our research questions.

Fusch and Ness ( 2015 : p. 1409) appear to endorse this somewhat inconsistent approach when advocating that the researcher should choose a sample size that has ‘the best opportunity for the researcher to reach data saturation’. Footnote 10

This tentative and equivocal commitment to saturation may reflect a practical response to the demands of funding bodies and ethics committees for a clear statement of sample size prior to starting a study (O’Reilly and Parker 2013 )—perceived obligations that, in practice, may be given priority over methodological considerations. However, it may also arise from the specific but somewhat uncertain logic that underlies saturation. Determining that further data collection or analysis is unnecessary on the basis of what has been concluded from data gathered hitherto is essentially a statement about the unobserved (what would have happened if the process of data collection and/or analysis had proceeded) based on the observed (the data collection and/or analysis that has taken place hitherto). Furthermore, if saturation is used in relation to negative case analysis in grounded theory (i.e. sources of data that may question or disconfirm aspects of the emergent theory) the logic becomes more tenuous—a statement about the unobserved based on the unobserved. Footnote 11 In either case, an uncertain predictive claim is made about the nature of data yet to be collected, and furthermore a claim that could only be tested if the decision to halt data collection were to be overturned. Additionally, the underlying reasoning makes specific assumptions about the way in which the analysis will generate theory, and the earlier in the process of theory development that this occurs the less warranted such assumptions may be. Accordingly, researchers who confidently propose saturation as a criterion for sampling at the outset of a study may become less certain as to how it should be operationalized once the study is in progress, and may therefore be reluctant to abide by it.

5 Conclusion

This paper has offered a critical reflection on the concept of saturation and its use in qualitative research, contributing to the small body of literature that has examined the complexities of the concept and its underlying assumptions. Drawing on recent examples of its use, saturation has been discussed in relation to three key sets of questions: What? Where and why? When and how?

Extending previous literature that has highlighted the variability in the use of saturation (O’Reilly and Parker 2013 ; Walker 2012 ), we have scrutinized the different ways in which it has been operationalized in the research literature, identifying four models of saturation, each of which appears to make different core assumptions about what saturation is, and about what exactly is being saturated. These have been labelled as: theoretical saturation, inductive thematic saturation, a priori thematic saturation, and data saturation. Moving forward, the identification and recognition of these different models of saturation may aid qualitative researchers in untangling some of the inconsistencies and contradictions that characterize its use.

Saturation’s apparent position as a ‘gold standard’ in assessing quality and its near universal application in qualitative research have been previously questioned (Guest et al. 2006 ; O’Reilly and Parker 2013 ; Malterud et al. 2016 ). Similarly, doubts have been raised regarding its common adoption as a sole criterion of the adequacy of data collection and analysis (Charmaz 2005 ), or of the adequacy of theory development: ‘Elegance, precision, coherence, and clarity are traditional criteria for evaluating theory, somewhat swamped by the metaphorical emphasis on saturation’ (Dey 2007 : p. 186). On the basis of such critiques, we have examined how saturation might be considered in relation to different theoretical and analytical approaches. Whilst we concur with the argument that saturation should not be afforded unquestioned status, polarization of saturation as either applicable or non-applicable to different approaches, as has been suggested (Walker 2012 ), may be too simplistic. Instead we propose that saturation has differing relevance, and a different meaning, depending on the role of theory, the analytic approach adopted, and so forth, and thus may usefully serve different purposes for different types of research—purposes that need to be clearly articulated by the researcher.

Whilst arguing for flexibility in terms of the purpose and use of saturation, we also suggest that there must be some limit to this range of purposes. Some of the ways in which saturation has been operationalized, we would suggest, risk stretching or diluting its meaning to the point where it becomes too widely encompassing, thereby undermining its coherence and utility.

When and how saturation may be judged to have been reached will differ depending on the type of study, as well as assumptions about whether it represents a distinct event or an ongoing process. The view of saturation as an event has been problematized by others (Strauss and Corbin 1998 ; Dey 1999 ; Nelson 2016 ), and we have explored the implications of conceptualizing saturation in this way, arguing that it appears to give rise to a degree of uncertainty and equivocation, in part driven by the uncertain logic of the concept itself—as a statement about the unobserved based on the observed. This uncertainty appears to give rise to inconsistencies and contradictions in its use, which we would argue could be resolved, at least in part, if saturation were to be considered as a matter of degree, rather than simply as something either attained or unattained. However, whilst considering saturation in incremental terms may increase researchers’ confidence in making claims to it, we suggest it is only through due consideration of the specific purpose for which saturation is being used, and what one is hoping to saturate, that the uncertainty around the concept can be resolved.

In highlighting and examining these areas of complexity, this paper has extended previous discussions of saturation in the literature. Whilst consideration of the concept has led some commentators to argue for the need for qualitative researchers to provide a more thorough and transparent reporting of how they achieved saturation in their research, thus allowing readers to assess the validity of this claim (Bowen 2008 ; Francis et al. 2010 ), our arguments go beyond this. We contend that there is a need not only for more transparent reporting, but also for a more thorough re-evaluation of how saturation is conceptualized and operationalized, including recognition of potential inconsistencies and contradictions in the use of the concept—this re-evaluation can be guided through attending to the four approaches we have identified and their implications for the purposes and uses of saturation. This may lead to a more consistent use of saturation, not in terms of its always being used in the same way, but in relation to consistency between the theoretical position and analytic framework adopted, allowing saturation to be used in such a way as to best meet the aims and objectives of the research. It is through consideration of such complexities in the context of specific approaches that saturation can have most value, enabling it to move away from its increasingly elevated yet uneasy position as a taken-for-granted convention of qualitative research.

Although primarily employed in primary research, principles of saturation have also been applied to qualitative synthesis (Garrett et al. 2012 ; Lipworth et al. 2013 ). However, our focus here is on its use in primary studies.

These authors proceed to make the more extreme claim that saturation ‘is important in any study, whether quantitative, qualitative, or mixed methods’ (Fusch and Ness 2015 : p. 1411).

It should be noted that Birks and Mills ( 2015 ) also state that, as part of theoretical saturation, ‘Categories are clearly articulated with sharply defined and dimensionalized properties’, suggesting a somewhat broader view of saturation, in which the nature of emerging themes is important, rather than just the fact of their (non)emergence.

This evokes Glaser’s criticism of Strauss’s approach to sampling, which he regards as conventional, rather than theoretical, sampling: ‘In conventional sampling the analyst questions, guesses and uses experience to go where he thinks he will have the data to test his hypotheses and find the theory that he has preconceived. Discovery to Strauss does not mean induction and emergence, it means finding his theory in data so that it can be tested’ (Glaser 1992 : p. 103).

Charmaz ( 2008 , 2014 ) is critical of the extension of the notion of saturation beyond the context of grounded theory, and in particular of its extension into what we here refer to as data saturation.

Few authors draw an explicit distinction between data and theoretical saturation—among the exceptions are Bowen ( 2008 ), Sandelowski ( 2008 ), O’Reilly and Parker ( 2013 ), and Hennink et al. ( 2017 ).

Hence, Dey ( 1999 : p. 117) suggests the term ‘sufficiency’ in preference to ‘saturation’.

This reflects Glaser and Strauss’s ( 1967 : p. 40) view of theory generation: ‘one is constantly alert to emergent perspectives that will change and help develop his theory. These perspectives can easily occur even on the final day of study or even when the manuscript is reviewed in page proof; so the published word is not the final one, but only a pause in the never-ending process of generating theory’.

On occasions, a reason for going beyond saturation appears to be ethical rather than methodological. Despite reaching saturation, France et al. ( 2008 : p. 22) note that owing to their ‘commitment to and respect for all the women who wanted to participate in the study, data collection did not end until all had been interviewed.’ Similarly, Kennedy et al. ( 2012 : p. 858) report that they exceeded saturation as this appeared to be ‘more ethical than purposefully choosing individuals to re-interview, or only interviewing until saturation’.

Bloor and Wood ( 2006 : p. 165) suggest that this tendency may stem from researchers feeling obliged to abide by sample sizes previously declared to funding bodies or ethics committees, whilst making claims to saturation in order to retain a sense of methodological credibility. Some authors—e.g. Guest et al. ( 2006 ), Francis et al. ( 2010 ), Hennink et al. ( 2017 )—have attempted for formulate procedures whereby the specific number of participants required to achieve saturation is calculated in advance.

The first logic is counter-inductive—future non-occurrences of data, codes or theoretical insights are posited on the basis of prior occurrences. In relation to negative case analysis, however, the logic becomes inductive—future non-occurrences are posited on the basis of prior non-occurrences.

Aiken, A.R.A., Dillaway, C., Mevs-Korff, N.: A blessing I can’t afford: factors underlying the paradox of happiness about unintended pregnancy. Soc. Sci. Med. 132 , 149–155 (2015)

Article   Google Scholar  

Birks, M., Mills, J.: Grounded Theory: A Practical Guide, 2nd edn. Sage, London (2015)

Google Scholar  

Bloor, M., Wood, F.: Keywords in Qualitative Methods: A Vocabulary of Research Concepts. Sage, London (2006)

Book   Google Scholar  

Boddy, C.R.: Sample size for qualitative research. Qual. Mark. J. 19 (4), 426–432 (2016)

Bowen, G.A.: Naturalistic inquiry and the saturation concept: a research note. Qual. Res. 8 (1), 137–152 (2008)

Bryman, A.: How many qualitative interviews is enough? In: Baker, S.E., Edwards, R. (eds.) How Many Qualitative Interviews is Enough? Expert Voices and Early Career Reflections on Sampling and Cases in Qualitative Research, pp. 18–20. ESRC National Centre for Research Methods, University of Southampton, Southampton (2012)

Charmaz, K.: Grounded theory in the 21st century: applications for advancing social justice studies. In: Denzin, N.K., Lincoln, Y.S. (eds.) The Sage Handbook of Qualitative Research, 3rd edn, pp. 507–535. Sage, Thousand Oaks (2005)

Charmaz, K.: Grounded theory as an emergent method. In: Hesse-Biber, S., Leavy, P. (eds.) Handbook of Emergent Methods, pp. 155–170. Guilford Press, New York (2008)

Charmaz, K.: Constructing Grounded Theory, 2nd edn. Sage, Thousand Oaks (2014)

Constantinou, C., Georgiou, M., Perdikogianni, M.: A comparative method for themes saturation (CoMeTS) in qualitative interviews. Qual. Res. (2017). doi: 10.1177/1468794116686650

Damschroder, L.J., Pritts, J.L., Neblo, M.A., Kalarickal, R.J., Creswell, J.W., Hayward, R.A.: Patients, privacy and trust: patients’ willingness to allow researchers to access their medical records. Soc. Sci. Med. 64 (1), 223–235 (2007)

Denny, E.: “I never know from one day to another how I will feel”: pain and uncertainty in women with endometriosis. Qual. Health Res. 19 (7), 985–995 (2009)

Dey, I.: Grounding Grounded Theory: Guidelines for Qualitative Inquiry. Academic Press, San Diego (1999)

Dey, I.: Grounding categories. In: Bryant, A., Charmaz, K. (eds.) The SAGE Handbook of Grounded Theory, pp. 167–190. Sage, Thousand Oaks (2007)

Drisko, J.W.: Strengthening qualitative studies and reports. J. Soc. Work Educ. 33 (1), 185–187 (1997)

Dubé, E., Vivion, M., Sauvageau, C., Gagneur, A., Gagnos, R., Guay, M.: “Nature does things well, why should we interfere?”: vaccine hesitancy among mothers. Qual. Health Res. 26 (3), 411–425 (2016)

Forsberg, A., Bäckman, L., Möller, A.: Experiencing liver transplantation: a phenomenological approach. J. Adv. Nurs. 32 (2), 327–334 (2000)

France, N.E.M., Farrell, K., Kearney, B., Myatt, S.: Women living with fibromyalgia: “do no harm”. Int. J. Hum. Caring 12 (4), 21–25 (2008)

Francis, J.J., Johnston, M., Robertson, C., Glidewell, L., Entwhistle, V., Eccles, M.P., Grimshaw, J.M.: What is an adequate sample size? Operationalising data saturation for theory-driven interview studies. Psychol. Health 25 (10), 1229–1245 (2010)

Fusch, P.I., Ness, L.R.: Are we there yet? Data saturation in qualitative research. Qual. Rep. 20 (9), 1408–1416 (2015)

Ganle, J.K.: Hegemonic masculinity, HIV/AIDS risk perception and sexual behavior change among young people in Ghana. Qual. Health Res. 26 (6), 763–781 (2016)

Garrett, C.R., Gask, L.L., Hays, R., Cherrington, A., Bundy, C., Dickens, C., Waheed, W., Coventry, P.A.: Accessing primary health care: a meta-ethnography of the experiences of British South Asian patients with diabetes, coronary heart disease or a mental health problem. Chronic Illn. 8 (2), 135–155 (2012)

Given, L.M.: 100 Questions (and Answers) About Qualitative Research. Sage, Thousand Oaks (2016)

Glaser, B.G.: Theoretical Sensitivity. Sociology Press, Mill Valley (1978)

Glaser, B.G.: Emergence vs Forcing: Basics of Grounded Theory Analysis. Sociology Press, Mill Valley (1992)

Glaser, B.G., Strauss, A.L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine, Chicago (1967)

Grady, M.P.: Qualitative and Action Research: A Practitioner Handbook. Phi Delta Kappa Educational Foundation, Bloomington (1998)

Goulding, C.: Grounded theory, ethnography and phenomenology: a comparative analysis of three qualitative strategies for marketing research. Eur. J. Mark. 39 (3/4), 294–308 (2005)

Guest, G., Bunce, A., Johnson, L.: How many interviews are enough? An experiment with data saturation and variability. Field Methods 18 (1), 59–82 (2006)

Hale, E.D., Treharne, G.J., Kitas, G.D.: Qualitative methodologies II: a brief guide to applying interpretative phenomenological analysis in musculoskeletal care. Musculoskelet. Care 6 (2), 86–96 (2007)

Hancock, M.E., Amankwaa, L., Revell, M.A., Mueller, D.: Focus group data saturation: a new approach to data analysis. Qual. Rep. 21 (11), 2124–2130 (2016)

Hawkins, R.L., Abrams, C.: Disappearing acts: the social networks of formerly homeless individuals with co-occurring disorders. Soc. Sci. Med. 65 (10), 2031–2042 (2007)

Hennink, M.M., Kaiser, B.N., Marconi, V.C.: Code saturation versus meaning saturation: how many interviews are enough? Qual. Health Res. 27 (4), 591–608 (2017)

Hill, C.L., Baird, W.O., Walters, S.J.: Quality of life in children and adolescents with osteogenesis imperfecta: a qualitative interview based study. Health Qual. Life Outcomes 12 , 54 (2014)

Jackson, D., Daly, J., Davidson, P., Elliott, D.: Women recovering from first-time myocardial infarction (MI): a feminist qualitative study. J. Adv. Nurs. 32 (6), 1403–1411 (2000)

Jackson, M., Harrison, P., Swinburn, B., Lawrence, M.: Using a qualitative vignette to explore a complex public health issue. Qual. Health Res. 25 (10), 1395–1409 (2015)

Jassim, G.A., Whitford, D.L.: Understanding the experiences and quality of life issues of Bahraini women with breast cancer. Soc. Sci. Med. 107 , 189–195 (2014)

Kazley, A.S., Johnson, E., Simpson, K., Chavin, K., Baliga, P.: African American patient knowledge of kidney disease: a qualitative study of those with advanced chronic kidney disease. Chronic Illn. 11 (4), 245–255 (2015)

Kennedy, F., Harcourt, D., Rumsey, N.: The shifting nature of women’s experiences and perceptions of ductal carcinoma in situ. J. Adv. Nurs. 68 (4), 856–867 (2012)

Legard, R., Keegan, J., Ward, K.: In-depth interviews. In: Ritchie, J., Lewis, J. (eds.) Qualitative Research Practice: A Guide for Social Science Students and Researchers, pp. 139–169. Sage, London (2003)

Leininger, M.: Evaluation criteria and critique of qualitative research studies. In: Morse, J.M. (ed.) Critical Issues in Qualitative Research Methods, pp. 95–115. Sage, Thousand Oaks (1994)

Lipworth, W., Taylor, N., Braithwaite, J.: Can the theoretical domains framework account for the implementation of clinical quality interventions? BMC Health Serv. Res. 13 , 530 (2013)

Long-Sutehall, T., Willis, H., Palmer, R., Ugboma, D., Addington-Hall, J., Coombs, M.: Negotiated dying: a grounded theory of how nurses shape withdrawal of treatment in hospital critical care units. Int. J. Nurs. Stud. 48 (12), 1466–1474 (2011)

Malterud, K., Siersma, V.D., Guassora, A.D.: Sample size in qualitative interview studies: guided by information power. Qual. Health Res. 26 (13), 1753–1760 (2016)

Marshall, V., Long, B.C.: Coping processes as revealed in the stories of mother of children with autism. Qual. Health Res. 20 (1), 105–116 (2010)

Mason, M.: Sample size and saturation in PhD studies using qualitative interviews. Forum Qual. Health Res. 11 (3), art 8 (2010)

McNulty, C., Ricketts, E.J., Rugman, C., Hogan, A., Charlett, A., Campbell, R.: On behalf of the CIRT study group: a qualitative study exploring the acceptability of the McNulty–Zelen design for randomised controlled trials evaluating educational interventions. BMC Family Pract. 16 , 169 (2015)

Middlemiss, T., Lloyd-Williams, M., Laird, B.J., Fallon, M.T.: Symptom control trials in patients with advanced cancer: a qualitative study. J. Pain Symptom Manag. 50 (5), 642–649 (2015)

Morse, J.M.: The significance of saturation. Qual. Health Res. 5 (2), 147–149 (1995)

Morse, J.M.: Data were saturated…. Qual. Health Res. 25 (5), 587–588 (2015)

Morse, J.M., Barrett, M., Mayan, M., Olson, K., Spiers, J.: Verification strategies for establishing reliability and validity in qualitative research. Int. J. Qual. Methods 1 (2), 1–19 (2002)

Naegeli, A.N., Flood, E., Tucker, J., Devlen, J., Edson-Heredia, E.: The patient experience with fatigue and content validity of a measure to assess fatigue severity: qualitative research in patients with ankylosing spondylitis (AS). Health Qual. Life Outcomes 11 , 192 (2013)

Nelson, J.: Using conceptual depth criteria: addressing the challenge of reaching saturation in qualitative research. Qual. Res. (2016). doi: 10.1177/1468794116679873

Niccolai, L.M., Hansen, C.E., Credle, M., Shapiro, E.D.: Parents’ recall and reflections on experiences related to HPV vaccination for their children. Qual. Health Res. 26 (6), 842–850 (2016)

Olshansky, E.F.: Generating theory using grounded theory methodology. In: de Chesnay, M. (ed.) Nursing Research Using Grounded Theory: Qualitative Designs and Methods in Nursing, pp. 19–28. Springer, New York (2015)

O’Reilly, M., Parker, N.: ‘Unsatisfactory saturation’: a critical exploration of the notion of saturated sample sizes in qualitative research. Qual. Res. 13 (2), 190–197 (2013)

Otmar, R., Kotowicz, M.A., Nicholson, G.C., Pasco, J.A.: Methodological reflections on using pilot data from fracture patients to develop a qualitative study. BMC Res. Notes 4 , 508 (2011)

Poletti, T., Balabanova, D., Ghazaryan, O., Kocharyan, H., Hakobyan, M., Arakelyan, K., Normand, C.: The desirability and feasibility of scaling up community health insurance in low-income settings—lessons from Armenia. Soc. Sci. Med. 64 (3), 509–520 (2007)

Power, T., Jackson, D., Carter, B., Weaver, R.: Misunderstood as mothers: women’s stories of being hospitalized for illness in the postpartum period. J. Adv. Nurs. 71 (2), 370–380 (2015)

Sandelowski, M.: Theoretical saturation. In: Given, L.M. (ed.) The SAGE Encyclopedia of Qualitative Research Methods, vol. 2, pp. 875–876. Sage, Thousand Oaks (2008)

Sparkes, E., Duarte, R.V., Raphael, J.H., Denny, E., Ashford, R.L.: Qualitative exploration of psychological factors associated with spinal cord stimulation outcome. Chronic Illn. 8 (4), 239–251 (2011)

Starks, H., Trinidad, S.B.: Choose your method: a comparison of phenomenology, discourse analysis, and grounded theory. Qual. Health Res. 17 (10), 1372–1380 (2007)

Strauss, A., Corbin, J.: Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 2nd edn. Sage, Thousand Oaks (1998)

Turner, A., Barlow, J., Ilbery, B.: Play hurt, live hurt: living with and managing osteoarthritis from the perspective of ex-professional footballers. J. Health Psychol. 7 (3), 285–301 (2002)

Tutton, E., Seers, K., Langstaff, D., Westwood, M.: Staff and patient views of the concept of hope on a stroke unit: a qualitative study. J. Adv. Nurs. 68 (9), 2061–2069 (2012)

Urquhart, C.: Grounded Theory for Qualitative Research: A Practical Guide. Sage, Thousand Oaks (2013)

Vandecasteele, T., Debyser, B., Van Hecke, A., De Backer, T., Beeckman, D., Verhaeghe, S.: Nurses’ perceptions of transgressive behaviour in care relationships: a qualitative study. J. Adv. Nurs. 71 (12), 2786–2798 (2015)

van Manen, M., Higgins, I., van der Reit, P.: A conversation with Max van Manen on phenomenology in its original sense. Nurs. Health Sci. 18 (1), 4–7 (2016)

Walker, J.L.: The use of saturation in qualitative research. Can. J. Cardiovasc. Nurs. 22 (12), 37–41 (2012)

Zhao, X., Davey, G.: Contesting modernity: tobacco use and romanticism among older Dai farmers in Xishuangbanna, China. Soc. Health Illn. 37 (8), 1173–1190 (2015)

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Acknowledgements

This paper has been informed by discussions with members of the social sciences group of the Institute for Primary Care and Health Sciences at Keele University. TK is funded by South Staffordshire and Shropshire NHS Foundation Trust. CJ is partly funded by NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands (CLAHRC, West Midlands); the views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

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Benjamin Saunders, Julius Sim, Tom Kingstone, Shula Baker, Bernadette Bartlam, Heather Burroughs & Clare Jinks

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Saunders, B., Sim, J., Kingstone, T. et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant 52 , 1893–1907 (2018). https://doi.org/10.1007/s11135-017-0574-8

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Saturation in qualitative research: exploring its conceptualization and operationalization

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• 1 1Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire ST5 5BG UK.
• 2 2School of Health Sciences, Queen Margaret University, Edinburgh, EH21 6UU UK.
• PMID: 29937585
• PMCID: PMC5993836
• DOI: 10.1007/s11135-017-0574-8

Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation-as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

Keywords: Data analysis; Data collection; Grounded theory; Qualitative research; Saturation.

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Patient medication management, understanding and adherence during the transition from hospital to outpatient care - a qualitative longitudinal study in polymorbid patients with type 2 diabetes

• Léa Solh Dost   ORCID: orcid.org/0000-0001-5767-1305 1 , 2 ,
• Giacomo Gastaldi   ORCID: orcid.org/0000-0001-6327-7451 3 &
• Marie P. Schneider   ORCID: orcid.org/0000-0002-7557-9278 1 , 2  

BMC Health Services Research volume  24 , Article number:  620 ( 2024 ) Cite this article

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Continuity of care is under great pressure during the transition from hospital to outpatient care. Medication changes during hospitalization may be poorly communicated and understood, compromising patient safety during the transition from hospital to home. The main aims of this study were to investigate the perspectives of patients with type 2 diabetes and multimorbidities on their medications from hospital discharge to outpatient care, and their healthcare journey through the outpatient healthcare system. In this article, we present the results focusing on patients’ perspectives of their medications from hospital to two months after discharge.

Patients with type 2 diabetes, with at least two comorbidities and who returned home after discharge, were recruited during their hospitalization. A descriptive qualitative longitudinal research approach was adopted, with four in-depth semi-structured interviews per participant over a period of two months after discharge. Interviews were based on semi-structured guides, transcribed verbatim, and a thematic analysis was conducted.

Twenty-one participants were included from October 2020 to July 2021. Seventy-five interviews were conducted. Three main themes were identified: (A) Medication management, (B) Medication understanding, and (C) Medication adherence, during three periods: (1) Hospitalization, (2) Care transition, and (3) Outpatient care. Participants had varying levels of need for medication information and involvement in medication management during hospitalization and in outpatient care. The transition from hospital to autonomous medication management was difficult for most participants, who quickly returned to their routines with some participants experiencing difficulties in medication adherence.

Conclusions

The transition from hospital to outpatient care is a challenging process during which discharged patients are vulnerable and are willing to take steps to better manage, understand, and adhere to their medications. The resulting tension between patients’ difficulties with their medications and lack of standardized healthcare support calls for interprofessional guidelines to better address patients’ needs, increase their safety, and standardize physicians’, pharmacists’, and nurses’ roles and responsibilities.

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Introduction

Continuity of patient care is characterized as the collaborative engagement between the patient and their physician-led care team in the ongoing management of healthcare, with the mutual objective of delivering high-quality and cost-effective medical care [ 1 ]. Continuity of care is under great pressure during the transition of care from hospital to outpatient care, with a risk of compromising patients’ safety [ 2 , 3 ]. The early post-discharge period is a high-risk and fragile transition: once discharged, one in five patients experience at least one adverse event during the first three weeks following discharge, and more than half of these adverse events are drug-related [ 4 , 5 ]. A retrospective study examining all discharged patients showed that adverse drug events (ADEs) account for up to 20% of 30-day hospital emergency readmissions [ 6 ]. During hospitalization, patients’ medications are generally modified, with an average of nearly four medication changes per patient [ 7 ]. Information regarding medications such as medication changes, the expected effect, side effects, and instructions for use are frequently poorly communicated to patients during hospitalization and at discharge [ 8 , 9 , 10 , 11 ]. Between 20 and 60% of discharged patients lack knowledge of their medications [ 12 , 13 ]. Consideration of patients’ needs and their active engagement in decision-making during hospitalization regarding their medications are often lacking [ 11 , 14 , 15 ]. This can lead to unsafe discharge and contribute to medication adherence difficulties, such as non-implementation of newly prescribed medications [ 16 , 17 ].

Patients with multiple comorbidities and polypharmacy are at higher risk of ADE [ 18 ]. Type 2 diabetes is one of the chronic health conditions most frequently associated with comorbidities and patients with type 2 diabetes often lack care continuum [ 19 , 20 , 21 ]. The prevalence of patients hospitalized with type 2 diabetes can exceed 40% [ 22 ] and these patients are at higher risk for readmission due to their comorbidities and their medications, such as insulin and oral hypoglycemic agents [ 23 , 24 , 25 ].

Interventions and strategies to improve patient care and safety at transition have shown mixed results worldwide in reducing cost, rehospitalization, ADE, and non-adherence [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. However, interventions that are patient-centered, with a patient follow-up and led by interprofessional healthcare teams showed promising results [ 34 , 35 , 36 ]. Most of these interventions have not been implemented routinely due to the extensive time to translate research into practice and the lack of hybrid implementation studies [ 37 , 38 , 39 , 40 , 41 ]. In addition, patient-reported outcomes and perspectives have rarely been considered, yet patients’ involvement is essential for seamless and integrated care [ 42 , 43 ]. Interprofessional collaboration in which patients are full members of the interprofessional team, is still in its infancy in outpatient care [ 44 ]. Barriers and facilitators regarding medications at the transition of care have been explored in multiple qualitative studies at one given time in a given setting (e.g., at discharge, one-month post-discharge) [ 8 , 45 , 46 , 47 , 48 ]. However, few studies have adopted a holistic methodology from the hospital to the outpatient setting to explore changes in patients’ perspectives over time [ 49 , 50 , 51 ]. Finally, little is known about whether, how, and when patients return to their daily routine following hospitalization and the impact of hospitalization weeks after discharge.

In Switzerland, continuity of care after hospital discharge is still poorly documented, both in terms of contextual analysis and interventional studies, and is mainly conducted in the hospital setting [ 31 , 35 , 52 , 53 , 54 , 55 , 56 ]. The first step of an implementation science approach is to perform a contextual analysis to set up effective interventions adapted to patients’ needs and aligned to healthcare professionals’ activities in a specific context [ 41 , 57 ]. Therefore, the main aims of this study were to investigate the perspectives of patients with type 2 diabetes and multimorbidities on their medications from hospital discharge to outpatient care, and on their healthcare journey through the outpatient healthcare system. In this article, we present the results focusing on patients’ perspectives of their medications from hospital to two months after discharge.

Study design

This qualitative longitudinal study, conducted from October 2020 to July 2021, used a qualitative descriptive methodology through four consecutive in-depth semi-structured interviews per participant at three, 10-, 30- and 60-days post-discharge, as illustrated in Fig.  1 . Longitudinal qualitative research is characterized by qualitative data collection at different points in time and focuses on temporality, such as time and change [ 58 , 59 ]. Qualitative descriptive studies aim to explore and describe the depth and complexity of human experiences or phenomena [ 60 , 61 , 62 ]. We focused our qualitative study on the 60 first days after discharge as this period is considered highly vulnerable and because studies often use 30- or 60-days readmission as an outcome measure [ 5 , 63 ].

This qualitative study follows the Consolidated Criteria for Reporting Qualitative Research (COREQ). Ethics committee approval was sought and granted by the Cantonal Research Ethics Commission, Geneva (CCER) (2020 − 01779).

Recruitment took place during participants’ hospitalization in the general internal medicine divisions at the Geneva University Hospitals in the canton of Geneva (500 000 inhabitants), Switzerland. Interviews took place at participants’ homes, in a private office at the University of Geneva, by telephone or by secure video call, according to participants’ preference. Informal caregivers could also participate alongside the participants.

Study flowchart

Researcher characteristics

All the researchers were trained in qualitative studies. The diabetologist and researcher (GG) who enrolled the patients in the study was involved directly or indirectly (advice asked to the Geneva University Hospital diabetes team of which he was a part) for most participants’ care during hospitalization. LS (Ph.D. student and community pharmacist) was unknown to participants and presented herself during hospitalization as a “researcher” and not as a healthcare professional to avoid any risk of influencing participants’ answers. This study was not interventional, and the interviewer (LS) invited participants to contact a healthcare professional for any questions related to their medication or medical issues.

Population and sampling strategy

Patients with type 2 diabetes were chosen as an example population to describe polypharmacy patients as these patients usually have several health issues and polypharmacy [ 20 , 22 , 25 ]. Inclusions criteria for the study were: adult patients with type 2 diabetes, with at least two other comorbidities, hospitalized for at least three days in a general internal medicine ward, with a minimum of one medication change during hospital stay, and who self-managed their medications once discharged home. Exclusion criteria were patients not reachable by telephone following discharge, unable to give consent (patients with schizophrenia, dementia, brain damage, or drug/alcohol misuse), and who could not communicate in French. A purposive sampling methodology was applied aiming to include participants with different ages, genders, types, and numbers of health conditions by listing participants’ characteristics in a double-entry table, available in Supplementary Material 1 , until thematic saturation was reached. Thematic saturation was considered achieved when no new code or theme emerged and new data repeated previously coded information [ 64 ]. The participants were identified if they were hospitalized in the ward dedicated to diabetes care or when the diabetes team was contacted for advice. The senior ward physician (GG) screened eligible patients and the interviewer (LS) obtained written consent before hospital discharge.

Data collection and instruments

Sociodemographic (age, gender, educational level, living arrangement) and clinical characteristics (reason for hospitalization, date of admission, health conditions, diabetes diagnosis, medications before and during hospitalization) were collected by interviewing participants before their discharge and by extracting participants’ data from electronic hospital files by GG and LS. Participants’ pharmacies were contacted with the participant’s consent to obtain medication records from the last three months if information regarding medications before hospitalization was missing in the hospital files.

Semi-structured interview guides for each interview (at three, 10-, 30- and 60-days post-discharge) were developed based on different theories and components of health behavior and medication adherence: the World Health Organization’s (WHO) five dimensions for adherence, the Information-Motivation-Behavioral skills model and the Social Cognitive Theory [ 65 , 66 , 67 ]. Each interview explored participants’ itinerary in the healthcare system and their perspectives on their medications. Regarding medications, the following themes were mentioned at each interview: changes in medications, patients’ understanding and implication; information on their medications, self-management of their medications, and patients’ medication adherence. Other aspects were mentioned in specific interviews: patients’ hospitalization and experience on their return home (interview 1), motivation (interviews 2 and 4), and patient’s feedback on the past two months (interview 4). Interview guides translated from French are available in Supplementary Material 2 . The participants completed self-reported and self-administrated questionnaires at different interviews to obtain descriptive information on different factors that may affect medication management and adherence: self-report questionnaires on quality of life (EQ-5D-5 L) [ 68 ], literacy (Schooling-Opinion-Support questionnaire) [ 69 ], medication adherence (Adherence Visual Analogue Scale, A-VAS) [ 70 ] and Belief in Medication Questionnaire (BMQ) [ 71 ] were administered to each participant at the end of selected interviews to address the different factors that may affect medication management and adherence as well as to determine a trend of determinants over time. The BMQ contains two subscores: Specific-Necessity and Specific-Concerns, addressing respectively their perceived needs for their medications, and their concerns about adverse consequences associated with taking their medication [ 72 ].

Data management

Informed consent forms, including consent to obtain health data, were securely stored in a private office at the University of Geneva. The participants’ identification key was protected by a password known only by MS and LS. Confidentiality was guaranteed by pseudonymization of participants’ information and audio-recordings were destroyed once analyzed. Sociodemographic and clinical characteristics, medication changes, and answers to questionnaires were securely collected by electronic case report forms (eCRFs) on RedCap®. Interviews were double audio-recorded and field notes were taken during interviews. Recorded interviews were manually transcribed verbatim in MAXQDA® (2018.2) by research assistants and LS and transcripts were validated for accuracy by LS. A random sample of 20% of questionnaires was checked for accuracy for the transcription from the paper questionnaires to the eCRFs. Recorded sequences with no link to the discussed topics were not transcribed and this was noted in the transcripts.

Data analysis

A descriptive statistical analysis of sociodemographic, clinical characteristics and self-reported questionnaire data was carried out. A thematic analysis of transcripts was performed, as described by Braun and Clarke [ 73 ], by following six steps: raw data was read, text segments related to the study objectives were identified, text segments to create new categories were identified, similar or redundant categories were reduced and a model that integrated all significant categories was created. The analysis was conducted in parallel with patient enrolment to ensure data saturation. To ensure the validity of the coding method, transcripts were double coded independently and discussed by the research team until similar themes were obtained. The research group developed and validated an analysis grid, with which LS coded systematically the transcriptions and met regularly with the research team to discuss questions on data analysis and to ensure the quality of coding. The analysis was carried out in French, and the verbatims of interest cited in the manuscript were translated and validated by a native English-speaking researcher to preserve the meaning.

In this analysis, we used the term “healthcare professionals” when more than one profession could be involved in participants’ medication management. Otherwise, when a specific healthcare professional was involved, we used the designated profession (e.g. physicians, pharmacists).

Patient and public involvement

During the development phase of the study, interview guides and questionnaires were reviewed for clarity and validity and adapted by two patient partners, with multiple health conditions and who experienced previously a hospital discharge. They are part of the HUG Patients Partners + 3P platform for research and patient and public involvement.

Interviews and participants’ descriptions

A total of 75 interviews were conducted with 21 participants. In total, 31 patients were contacted, seven refused to participate (four at the project presentation and three at consent), two did not enter the selection criteria at discharge and one was unreachable after discharge. Among the 21 participants, 15 participated in all interviews, four in three interviews, one in two interviews, and one in one interview, due to scheduling constraints. Details regarding interviews and participants characteristics are presented in Tables  1 and 2 .

The median length of time between hospital discharge and interviews 1,2,3 and 4 was 5 (IQR: 4–7), 14 (13-20), 35 (22-38), and 63 days (61-68), respectively. On average, by comparing medications at hospital admission and discharge, a median of 7 medication changes (IQR: 6–9, range:2;17) occurred per participant during hospitalization and a median of 7 changes (5–12) during the two months following discharge. Details regarding participants’ medications are described in Table  3 .

Patient self-reported adherence over the past week for their three most challenging medications are available in Supplementary Material 3 .

Qualitative analysis

We defined care transition as the period from discharge until the first medical appointment post-discharge, and outpatient care as the period starting after the first medical appointment. Data was organized into three key themes (A. Medication management, B. Medication understanding, and C. Medication adherence) divided into subthemes at three time points (1. Hospitalization, 2. Care transition and 3. Outpatient care). Figure  2 summarizes and illustrates the themes and subthemes with their influencing factors as bullet points.

Participants’ medication management, understanding and adherence during hospitalization, care transition and outpatient care

A. Medication management

A.1 medication management during hospitalization: medication management by hospital staff.

Medications during hospitalization were mainly managed by hospital healthcare professionals (i.e. nurses and physicians) with varying degrees of patient involvement: “At the hospital, they prepared the medications for me. […] I didn’t even know what the packages looked like.” Participant 22; interview 1 (P22.1) Some participants reported having therapeutic education sessions with specialized nurses and physicians, such as the explanation and demonstration of insulin injection and glucose monitoring. A patient reported that he was given the choice of several treatments and was involved in shared decision-making. Other participants had an active role in managing and optimizing dosages, such as rapid insulin, due to prior knowledge and use of medications before hospitalization.

A.2 Medication management at transition: obtaining the medication and initiating self-management

Once discharged, some participants had difficulties obtaining their medications at the pharmacy because some medications were not stored and had to be ordered, delaying medication initiation. To counter this problem upstream, a few participants were provided a 24-to-48-hour supply of medications at discharge. It was sometimes requested by the patient or suggested by the healthcare professionals but was not systematic. The transition from medication management by hospital staff to self-management was exhausting for most participants who were faced with a large amount of new information and changes in their medications: “ When I was in the hospital, I didn’t even realize all the changes. When I came back home, I took away the old medication packages and got out the new ones. And then I thought : « my God, all this…I didn’t know I had all these changes » ” P2.1 Written documentation, such as the discharge prescription or dosage labels on medication packages, was helpful in managing their medication at home. Most participants used weekly pill organizers to manage their medications, which were either already used before hospitalization or were introduced post-discharge. The help of a family caregiver in managing and obtaining medications was reported as a facilitator.

A.3 Medication management in outpatient care: daily self-management and medication burden

A couple of days or weeks after discharge, most participants had acquired a routine so that medication management was less demanding, but the medication burden varied depending on the participants. For some, medication management became a simple action well implemented in their routine (“It has become automatic” , P23.4), while for others, the number of medications and the fact that the medications reminded them of the disease was a heavy burden to bear on a daily basis (“ During the first few days after getting out of the hospital, I thought I was going to do everything right. In the end, well [laughs] it’s complicated. I ended up not always taking the medication, not monitoring the blood sugar” P12.2) To support medication self-management, some participants had written documentation such as treatment plans, medication lists, and pictures of their medication packages on their phones. Some participants had difficulties obtaining medications weeks after discharge as discharge prescriptions were not renewable and participants did not see their physician in time. Others had to visit multiple physicians to have their prescriptions updated. A few participants were faced with prescription or dispensing errors, such as prescribing or dispensing the wrong dosage, which affected medication management and decreased trust in healthcare professionals. In most cases, according to participants, the pharmacy staff worked in an interprofessional collaboration with physicians to provide new and updated prescriptions.

B. Medication understanding

B.1 medication understanding during hospitalization: new information and instructions.

The amount of information received during hospitalization varied considerably among participants with some reporting having received too much, while others saying they received too little information regarding medication changes, the reason for changes, or for introducing new medications: “They told me I had to take this medication all my life, but they didn’t tell me what the effects were or why I was taking it.” P5.3

Hospitalization was seen by some participants as a vulnerable and tiring period during which they were less receptive to information. Information and explanations were generally given verbally, making it complicated for most participants to recall it. Some participants reported that hospital staff was attentive to their needs for information and used communication techniques such as teach-back (a way of checking understanding by asking participants to say in their own words what they need to know or do about their health or medications). Some participants were willing to be proactive in the understanding of their medications while others were more passive, had no specific needs for information, and did not see how they could be engaged more.

B.2 Medication understanding at transition: facing medication changes

At hospital discharge, the most challenging difficulty for participants was to understand the changes made regarding their medications. For newly diagnosed participants, the addition of new medications was more difficult to understand, whereas, for experienced participants, changes in known medications such as dosage modification, changes within a therapeutic class, and generic substitutions were the most difficult to understand. Not having been informed about changes caused confusion and misunderstanding. Therefore, medication reconciliation done by the patient was time-consuming, especially for participants with multiple medications: “ They didn’t tell me at all that they had changed my treatment completely. They just told me : « We’ve changed a few things. But it was the whole treatment ». ” P2.3 Written information, such as the discharge prescription, the discharge report (brief letter summarizing information about the hospitalization, given to the patient at discharge), or the label on the medication box (written by the pharmacist with instructions on dosage) helped them find or recall information about their medications and diagnoses. However, technical terms were used in hospital documentations and were not always understandable. For example, this participant said: “ On the prescription of valsartan, they wrote: ‘resume in the morning once profile…’[once hypertension profile allows]… I don’t know what that means.” P8.1 In addition, some documents were incomplete, as mentioned by a patient who did not have the insulin dosage mentioned on the hospital prescription. Some participants sought help from healthcare professionals, such as pharmacists, hospital physicians, or general practitioners a few days after discharge to review medications, answer questions, or obtain additional information.

B.3 Medication understanding in the outpatient care: concerns and knowledge

Weeks after discharge, most participants had concerns about the long-term use of their medications, their usefulness, and the possible risk of interactions or side effects. Some participants also reported having some lack of knowledge regarding indications, names, or how the medication worked: “I don’t even know what Brilique® [ticagrelor, antiplatelet agent] is for. It’s for blood pressure, isn’t it?. I don’t know.” P11.4 According to participants, the main reasons for the lack of understanding were the lack of information at the time of prescribing and the large number of medications, making it difficult to search for information and remember it. Participants sought information from different healthcare professionals or by themselves, on package inserts, through the internet, or from family and friends. Others reported having had all the information needed or were not interested in having more information. In addition, participants with low medication literacy, such as non-native speakers or elderly people, struggled more with medication understanding and sought help from family caregivers or healthcare professionals, even weeks after discharge: “ I don’t understand French very well […] [The doctor] explained it very quickly…[…] I didn’t understand everything he was saying” P16.2

C. Medication adherence

C.2 medication adherence at transition: adopting new behaviors.

Medication adherence was not mentioned as a concern during hospitalization and a few participants reported difficulties in medication initiation once back home: “I have an injection of Lantus® [insulin] in the morning, but obviously, the first day [after discharge], I forgot to do it because I was not used to it.” P23.1 Participants had to quickly adopt new behaviors in the first few days after discharge, especially for participants with few medications pre-hospitalization. The use of weekly pill organizers, alarms and specific storage space were reported as facilitators to support adherence. One patient did not initiate one of his medications because he did not understand the medication indication, and another patient took her old medications because she was used to them. Moreover, most participants experienced their hospitalization as a turning point, a time when they focused on their health, thought about the importance of their medications, and discussed any new lifestyle or dietary measures that might be implemented.

C.3 Medication adherence in outpatient care: ongoing medication adherence

More medication adherence difficulties appeared a few weeks after hospital discharge when most participants reported nonadherence behaviors, such as difficulties implementing the dosage regimen, or intentionally discontinuing the medication and modifying the medication regimen on their initiative. Determinants positively influencing medication adherence were the establishment of a routine; organizing medications in weekly pill-organizers; organizing pocket doses (medications for a short period that participants take with them when away from home); seeking support from family caregivers; using alarm clocks; and using specific storage places. Reasons for nonadherence were changes in daily routine; intake times that were not convenient for the patient; the large number of medications; and poor knowledge of the medication or side effects. Healthcare professionals’ assistance for medication management, such as the help of home nurses or pharmacists for the preparation of weekly pill-organizers, was requested by participants or offered by healthcare professionals to support medication adherence: “ I needed [a home nurse] to put my pills in the pillbox. […] I felt really weak […] and I was making mistakes. So, I’m very happy [the doctor] offered me [home care]. […] I have so many medications.” P22.3 Some participants who experienced prehospitalization non-adherence were more aware of their non-adherence and implemented strategies, such as modifying the timing of intake: “I said to my doctor : « I forget one time out of two […], can I take them in the morning? » We looked it up and yes, I can take it in the morning.” P11.2 In contrast, some participants were still struggling with adherence difficulties that they had before hospitalization. Motivations for taking medications two months after discharge were to improve health, avoid complications, reduce symptoms, reduce the number of medications in the future or out of obligation: “ I force myself to take them because I want to get to the end of my diabetes, I want to reduce the number of pills as much as possible.” P14.2 After a few weeks post-hospitalization, for some participants, health and illness were no longer the priority because of other life imperatives (e.g., family or financial situation).

This longitudinal study provided a multi-faceted representation of how patients manage, understand, and adhere to their medications from hospital discharge to two months after discharge. Our findings highlighted the varying degree of participants’ involvement in managing their medications during their hospitalization, the individualized needs for information during and after hospitalization, the complicated transition from hospital to autonomous medication management, the adaptation of daily routines around medication once back home, and the adherence difficulties that surfaced in the outpatient care, with nonadherence prior to hospitalization being an indicator of the behavior after discharge. Finally, our results confirmed the lack of continuity in care and showed the lack of patient care standardization experienced by the participants during the transition from hospital to outpatient care.

This in-depth analysis of patients’ experiences reinforces common challenges identified in the existing literature such as the lack of personalized information [ 9 , 10 , 11 ], loss of autonomy during hospitalization [ 14 , 74 , 75 ], difficulties in obtaining medication at discharge [ 11 , 45 , 76 ] and challenges in understanding treatment modifications and generics substitution [ 11 , 32 , 77 , 78 ]. Some of these studies were conducted during patients’ hospitalization [ 10 , 75 , 79 ] or up to 12 months after discharge [ 80 , 81 ], but most studies focused on the few days following hospital discharge [ 9 , 11 , 14 , 82 ]. Qualitative studies on medications at transition often focused on a specific topic, such as medication information, or a specific moment in time, and often included healthcare professionals, which muted patients’ voices [ 9 , 10 , 11 , 47 , 49 ]. Our qualitative longitudinal methodology was interested in capturing the temporal dynamics, in-depth narratives, and contextual nuances of patients’ medication experiences during transitions of care [ 59 , 83 ]. This approach provided a comprehensive understanding of how patients’ perspectives and behaviors evolved over time, offering insights into the complex interactions of medication management, understanding and adherence, and turning points within their medication journeys. A qualitative longitudinal design was used by Fylan et al. to underline patients’ resilience in medication management during and after discharge, by Brandberg et al. to show the dynamic process of self-management during the 4 weeks post-discharge and by Lawton et al. to examine how patients with type 2 diabetes perceived their care after discharge over a period of four years [ 49 , 50 , 51 ]. Our study focused on the first two months following hospitalization and future studies should focus on following discharged and at-risk patients over a longer period, as “transitions of care do not comprise linear trajectories of patients’ movements, with a starting and finishing point. Instead, they are endless loops of movements” [ 47 ].

Our results provide a particularly thorough description of how participants move from a state of total dependency during hospitalization regarding their medication management to a sudden and complete autonomy after hospital discharge impacting medication management, understanding, and adherence in the first days after discharge for some participants. Several qualitative studies have described the lack of shared decision-making and the loss of patient autonomy during hospitalization, which had an impact on self-management and created conflicts with healthcare professionals [ 75 , 81 , 84 ]. Our study also highlights nuanced patient experiences, including varying levels of patient needs, involvement, and proactivity during hospitalization and outpatient care, and our results contribute to capturing different perspectives that contrast with some literature that often portrays patients as more passive recipients of care [ 14 , 15 , 74 , 75 ]. Shared decision-making and proactive medication are key elements as they contribute to a smoother transition and better outcomes for patients post-discharge [ 85 , 86 , 87 ].

Consistent with the literature, the study identifies some challenges in medication initiation post-discharge [ 16 , 17 , 88 ] but our results also describe how daily routine rapidly takes over, either solidifying adherence behavior or generating barriers to medication adherence. Participants’ nonadherence prior to hospitalization was a factor influencing participants’ adherence post-hospitalization and this association should be further investigated, as literature showed that hospitalized patients have high scores of non-adherence [ 89 ]. Mortel et al. showed that more than 20% of discharged patients stopped their medications earlier than agreed with the physician and 25% adapted their medication intake [ 90 ]. Furthermore, patients who self-managed their medications had a lower perception of the necessity of their medication than patients who received help, which could negatively impact medication adherence [ 91 ]. Although participants in our study had high BMQ scores for necessity and lower scores for concerns, some participants expressed doubts about the need for their medications and a lack of motivation a few weeks after discharge. Targeted pharmacy interventions for newly prescribed medications have been shown to improve medication adherence, and hospital discharge is an opportune moment to implement this service [ 92 , 93 ].

Many medication changes were made during the transition of care (a median number of 7 changes during hospitalization and 7 changes during the two months after discharge), especially medication additions during hospitalization and interruptions after hospitalization. While medication changes during hospitalization are well described, the many changes following discharge are less discussed [ 7 , 94 ]. A Danish study showed that approximately 65% of changes made during hospitalization were accepted by primary healthcare professionals but only 43% of new medications initiated during hospitalization were continued after discharge [ 95 ]. The numerous changes after discharge may be caused by unnecessary intensification of medications during hospitalization, delayed discharge letters, lack of standardized procedures, miscommunication, patient self-management difficulties, or in response to an acute situation [ 96 , 97 , 98 ]. During the transition of care, in our study, both new and experienced participants were faced with difficulties in managing and understanding medication changes, either for newly prescribed medication or changes in previous medications. Such difficulties corroborate the findings of the literature [ 9 , 10 , 47 ] and our results showed that the lack of understanding during hospitalization led to participants having questions about their medications, even weeks after discharge. Particular attention should be given to patients’ understanding of medication changes jointly by physicians, nurses and pharmacists during the transition of care and in the months that follow as medications are likely to undergo as many changes as during hospitalization.

Implication for practice and future research

The patients’ perspectives in this study showed, at a system level, that there was a lack of standardization in healthcare professional practices regarding medication dispensing and follow-up. For now, in Switzerland, there are no official guidelines on medication prescription and dispensation during the transition of care although some international guidelines have been developed for outpatient healthcare professionals [ 3 , 99 , 100 , 101 , 102 ]. Here are some suggestions for improvement arising from our results. Patients should be included as partners and healthcare professionals should systematically assess (i) previous medication adherence, (ii) patients’ desired level of involvement and (iii) their needs for information during hospitalization. Hospital discharge processes should be routinely implemented to standardize hospital discharge preparation, medication prescribing, and dispensing. Discharge from the hospital should be planned with community pharmacies to ensure that all medications are available and, if necessary, doses of medications should be supplied by the hospital to bridge the gap. A partnership with outpatient healthcare professionals, such as general practitioners, community pharmacists, and homecare nurses, should be set up for effective asynchronous interprofessional collaboration to consolidate patients’ medication management, knowledge, and adherence, as well as to monitor signs of deterioration or adverse drug events.

Future research should consolidate our first attempt to develop a framework to better characterize medication at the transition of care, using Fig. 2   as a starting point. Contextualized interventions, co-designed by health professionals, patients and stakeholders, should be tested in a hybrid implementation study to test the implementation and effectiveness of the intervention for the health system [ 103 ].

Limitations

This study has some limitations. First, the transcripts were validated for accuracy by the interviewer but not by a third party, which could have increased the robustness of the transcription. Nevertheless, the interviewer followed all methodological recommendations for transcription. Second, patient inclusion took place during the COVID-19 pandemic, which may have had an impact on patient care and the availability of healthcare professionals. Third, we cannot guarantee the accuracy of some participants’ medication history before hospitalization, even though we contacted the participants’ main pharmacy, as participants could have gone to different pharmacies to obtain their medications. Fourth, our findings may not be generalizable to other populations and other healthcare systems because some issues may be specific to multimorbid patients with type 2 diabetes or to the Swiss healthcare setting. Nevertheless, issues encountered by our participants regarding their medications correlate with findings in the literature. Fifth, only 15 out of 21 participants took part in all the interviews, but most participants took part in at least three interviews and data saturation was reached. Lastly, by its qualitative and longitudinal design, it is possible that the discussion during interviews and participants’ reflections between interviews influenced participants’ management, knowledge, and adherence, even though this study was observational, and no advice or recommendations were given by the interviewer during interviews.

Discharged patients are willing to take steps to better manage, understand, and adhere to their medications, yet they are also faced with difficulties in the hospital and outpatient care. Furthermore, extensive changes in medications not only occur during hospitalization but also during the two months following hospital discharge, for which healthcare professionals should give particular attention. The different degrees of patients’ involvement, needs and resources should be carefully considered to enable them to better manage, understand and adhere to their medications. At a system level, patients’ experiences revealed a lack of standardization of medication practices during the transition of care. The healthcare system should provide the ecosystem needed for healthcare professionals responsible for or involved in the management of patients’ medications during the hospital stay, discharge, and outpatient care to standardize their practices while considering the patient as an active partner.

Data availability

The anonymized quantitative survey datasets and the qualitative codes are available in French from the corresponding author on reasonable request.

Abbreviations

adverse drug events

Adherence Visual Analogue Scale

Belief in Medication Questionnaire

Consolidated Criteria for Reporting Qualitative Research

case report form

standard deviation

World Health Organization

American Academy of Family Physician. Continuity of Care, Definition of 2020. Accessed 10 July 2022 https://www.aafp.org/about/policies/all/continuity-of-care-definition.html

Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–41.

Article   CAS   PubMed   Google Scholar  

World Health Organization (WHO). Medication Safety in Transitions of Care. 2019.

Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–7.

Article   PubMed   Google Scholar  

Krumholz HM. Post-hospital syndrome–an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–2.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Banholzer S, Dunkelmann L, Haschke M, Derungs A, Exadaktylos A, Krähenbühl S, et al. Retrospective analysis of adverse drug reactions leading to short-term emergency hospital readmission. Swiss Med Wkly. 2021;151:w20400.

Blozik E, Signorell A, Reich O. How does hospitalization affect continuity of drug therapy: an exploratory study. Ther Clin Risk Manag. 2016;12:1277–83.

Article   PubMed   PubMed Central   Google Scholar  

Allen J, Hutchinson AM, Brown R, Livingston PM. User experience and care for older people transitioning from hospital to home: patients’ and carers’ perspectives. Health Expect. 2018;21(2):518–27.

Daliri S, Bekker CL, Buurman BM, Scholte Op Reimer WJM, van den Bemt BJF, Karapinar-Çarkit F. Barriers and facilitators with medication use during the transition from hospital to home: a qualitative study among patients. BMC Health Serv Res. 2019;19(1):204.

Bekker CL, Mohsenian Naghani S, Natsch S, Wartenberg NS, van den Bemt BJF. Information needs and patient perceptions of the quality of medication information available in hospitals: a mixed method study. Int J Clin Pharm. 2020;42(6):1396–404.

Foulon V, Wuyts J, Desplenter F, Spinewine A, Lacour V, Paulus D, et al. Problems in continuity of medication management upon transition between primary and secondary care: patients’ and professionals’ experiences. Acta Clin Belgica: Int J Clin Lab Med. 2019;74(4):263–71.

Article   Google Scholar  

Micheli P, Kossovsky MP, Gerstel E, Louis-Simonet M, Sigaud P, Perneger TV, et al. Patients’ knowledge of drug treatments after hospitalisation: the key role of information. Swiss Med Wkly. 2007;137(43–44):614–20.

PubMed   Google Scholar  

Ziaeian B, Araujo KL, Van Ness PH, Horwitz LI. Medication reconciliation accuracy and patient understanding of intended medication changes on hospital discharge. J Gen Intern Med. 2012;27(11):1513–20.

Allen J, Hutchinson AM, Brown R, Livingston PM. User experience and care integration in Transitional Care for older people from hospital to home: a Meta-synthesis. Qual Health Res. 2016;27(1):24–36.

Mackridge AJ, Rodgers R, Lee D, Morecroft CW, Krska J. Cross-sectional survey of patients’ need for information and support with medicines after discharge from hospital. Int J Pharm Pract. 2018;26(5):433–41.

Mulhem E, Lick D, Varughese J, Barton E, Ripley T, Haveman J. Adherence to medications after hospital discharge in the elderly. Int J Family Med. 2013;2013:901845.

Fallis BA, Dhalla IA, Klemensberg J, Bell CM. Primary medication non-adherence after discharge from a general internal medicine service. PLoS ONE. 2013;8(5):e61735.

Zhou L, Rupa AP. Categorization and association analysis of risk factors for adverse drug events. Eur J Clin Pharmacol. 2018;74(4):389–404.

Moreau-Gruet F. La multimorbidité chez les personnes de 50 ans et plus. Résultats basés sur l’enqête SHARE (Survey of Health, Ageing and Retirement in Europe. Obsan Bulletin 4/2013. 2013(Neuchâtel: OBservatoire suisse de la santé).

Iglay K, Hannachi H, Joseph Howie P, Xu J, Li X, Engel SS, et al. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Curr Med Res Opin. 2016;32(7):1243–52.

Sibounheuang P, Olson PS, Kittiboonyakun P. Patients’ and healthcare providers’ perspectives on diabetes management: a systematic review of qualitative studies. Res Social Adm Pharm. 2020;16(7):854–74.

Müller-Wieland D, Merkel M, Hamann A, Siegel E, Ottillinger B, Woker R, et al. Survey to estimate the prevalence of type 2 diabetes mellitus in hospital patients in Germany by systematic HbA1c measurement upon admission. Int J Clin Pract. 2018;72(12):e13273.

Blanc AL, Fumeaux T, Stirnemann J, Dupuis Lozeron E, Ourhamoune A, Desmeules J, et al. Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients. PLoS ONE. 2019;14(7):e0219348.

Hansen LO, Greenwald JL, Budnitz T, Howell E, Halasyamani L, Maynard G, et al. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med. 2013;8(8):421–7.

Khalid JM, Raluy-Callado M, Curtis BH, Boye KS, Maguire A, Reaney M. Rates and risk of hospitalisation among patients with type 2 diabetes: retrospective cohort study using the UK General Practice Research Database linked to English Hospital Episode statistics. Int J Clin Pract. 2014;68(1):40–8.

Lussier ME, Evans HJ, Wright EA, Gionfriddo MR. The impact of community pharmacist involvement on transitions of care: a systematic review and meta-analysis. J Am Pharm Assoc. 2020;60(1):153–.

van der Heijden A, de Bruijne MC, Nijpels G, Hugtenburg JG. Cost-effectiveness of a clinical medication review in vulnerable older patients at hospital discharge, a randomized controlled trial. Int J Clin Pharm. 2019;41(4):963–71.

Bingham J, Campbell P, Schussel K, Taylor AM, Boesen K, Harrington A, et al. The Discharge Companion Program: an interprofessional collaboration in Transitional Care Model Delivery. Pharm (Basel). 2019;7(2):68.

Google Scholar  

Farris KB, Carter BL, Xu Y, Dawson JD, Shelsky C, Weetman DB, et al. Effect of a care transition intervention by pharmacists: an RCT. BMC Health Serv Res. 2014;14:406.

Meslot C, Gauchet A, Hagger MS, Chatzisarantis N, Lehmann A, Allenet B. A Randomised Controlled Trial to test the effectiveness of planning strategies to improve Medication Adherence in patients with Cardiovascular Disease. Appl Psychol Health Well Being. 2017;9(1):106–29.

Garnier A, Rouiller N, Gachoud D, Nachar C, Voirol P, Griesser AC, et al. Effectiveness of a transition plan at discharge of patients hospitalized with heart failure: a before-and-after study. ESC Heart Fail. 2018;5(4):657–67.

Daliri S, Bekker CL, Buurman BM, Scholte Op Reimer WJM, van den Bemt BJF, Karapinar-Çarkit F. Medication management during transitions from hospital to home: a focus group study with hospital and primary healthcare providers in the Netherlands. Int J Clin Pharm. 2020.

Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520–8.

Leppin AL, Gionfriddo MR, Kessler M, Brito JP, Mair FS, Gallacher K, et al. Preventing 30-day hospital readmissions: a systematic review and meta-analysis of randomized trials. JAMA Intern Med. 2014;174(7):1095–107.

Donzé J, John G, Genné D, Mancinetti M, Gouveia A, Méan M et al. Effects of a Multimodal Transitional Care Intervention in patients at high risk of readmission: the TARGET-READ Randomized Clinical Trial. JAMA Intern Med. 2023.

Rodrigues CR, Harrington AR, Murdock N, Holmes JT, Borzadek EZ, Calabro K, et al. Effect of pharmacy-supported transition-of-care interventions on 30-Day readmissions: a systematic review and Meta-analysis. Ann Pharmacother. 2017;51(10):866–89.

Lam MYY, Dodds LJ, Corlett SA. Engaging patients to access the community pharmacy medicine review service after discharge from hospital: a cross-sectional study in England. Int J Clin Pharm. 2019;41(4):1110–7.

Hossain LN, Fernandez-Llimos F, Luckett T, Moullin JC, Durks D, Franco-Trigo L, et al. Qualitative meta-synthesis of barriers and facilitators that influence the implementation of community pharmacy services: perspectives of patients, nurses and general medical practitioners. BMJ Open. 2017;7(9):e015471.

En-Nasery-de Heer S, Uitvlugt EB, Bet PM, van den Bemt BJF, Alai A, van den Bemt P et al. Implementation of a pharmacist-led transitional pharmaceutical care programme: process evaluation of medication actions to reduce hospital admissions through a collaboration between Community and Hospital pharmacists (MARCH). J Clin Pharm Ther. 2022.

Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510–20.

De Geest S, Zúñiga F, Brunkert T, Deschodt M, Zullig LL, Wyss K, et al. Powering Swiss health care for the future: implementation science to bridge the valley of death. Swiss Med Wkly. 2020;150:w20323.

Noonan VK, Lyddiatt A, Ware P, Jaglal SB, Riopelle RJ, Bingham CO 3, et al. Montreal Accord on patient-reported outcomes (PROs) use series - paper 3: patient-reported outcomes can facilitate shared decision-making and guide self-management. J Clin Epidemiol. 2017;89:125–35.

Hesselink G, Schoonhoven L, Barach P, Spijker A, Gademan P, Kalkman C, et al. Improving patient handovers from hospital to primary care: a systematic review. Ann Intern Med. 2012;157(6):417–28.

(OFSP) Interprofessionnalité dans le domaine de la santé Soins ambulatoire. Accessed 4 January 2024. https://www.bag.admin.ch/bag/fr/home/strategie-und-politik/nationale-gesundheitspolitik/foerderprogramme-der-fachkraefteinitiative-plus/foerderprogramme-interprofessionalitaet.html

Mitchell SE, Laurens V, Weigel GM, Hirschman KB, Scott AM, Nguyen HQ, et al. Care transitions from patient and caregiver perspectives. Ann Fam Med. 2018;16(3):225–31.

Davoody N, Koch S, Krakau I, Hägglund M. Post-discharge stroke patients’ information needs as input to proposing patient-centred eHealth services. BMC Med Inf Decis Mak. 2016;16:66.

Ozavci G, Bucknall T, Woodward-Kron R, Hughes C, Jorm C, Joseph K, et al. A systematic review of older patients’ experiences and perceptions of communication about managing medication across transitions of care. Res Social Adm Pharm. 2021;17(2):273–91.

Fylan B, Armitage G, Naylor D, Blenkinsopp A. A qualitative study of patient involvement in medicines management after hospital discharge: an under-recognised source of systems resilience. BMJ Qual Saf. 2018;27(7):539–46.

Fylan B, Marques I, Ismail H, Breen L, Gardner P, Armitage G, et al. Gaps, traps, bridges and props: a mixed-methods study of resilience in the medicines management system for patients with heart failure at hospital discharge. BMJ Open. 2019;9(2):e023440.

Brandberg C, Ekstedt M, Flink M. Self-management challenges following hospital discharge for patients with multimorbidity: a longitudinal qualitative study of a motivational interviewing intervention. BMJ Open. 2021;11(7):e046896.

Lawton J, Rankin D, Peel E, Douglas M. Patients’ perceptions and experiences of transitions in diabetes care: a longitudinal qualitative study. Health Expect. 2009;12(2):138–48.

Mabire C, Bachnick S, Ausserhofer D, Simon M. Patient readiness for hospital discharge and its relationship to discharge preparation and structural factors: a cross-sectional study. Int J Nurs Stud. 2019;90:13–20.

Meyers DC, Durlak JA, Wandersman A. The quality implementation framework: a synthesis of critical steps in the implementation process. Am J Community Psychol. 2012;50(3–4):462–80.

Meyer-Massetti C, Hofstetter V, Hedinger-Grogg B, Meier CR, Guglielmo BJ. Medication-related problems during transfer from hospital to home care: baseline data from Switzerland. Int J Clin Pharm. 2018;40(6):1614–20.

Neeman M, Dobrinas M, Maurer S, Tagan D, Sautebin A, Blanc AL, et al. Transition of care: a set of pharmaceutical interventions improves hospital discharge prescriptions from an internal medicine ward. Eur J Intern Med. 2017;38:30–7.

Geese F, Schmitt KU. Interprofessional Collaboration in Complex Patient Care Transition: a qualitative multi-perspective analysis. Healthc (Basel). 2023;11(3).

Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. Int J Nurs Stud. 2013;50(5):587–92.

Thomson R, Plumridge L, Holland J, Editorial. Int J Soc Res Methodol. 2003;6(3):185–7.

Audulv Å, Hall EOC, Kneck Å, Westergren T, Fegran L, Pedersen MK, et al. Qualitative longitudinal research in health research: a method study. BMC Med Res Methodol. 2022;22(1):255.

Kim H, Sefcik JS, Bradway C. Characteristics of qualitative descriptive studies: a systematic review. Res Nurs Health. 2017;40(1):23–42.

Sandelowski M. Whatever happened to qualitative description? Res Nurs Health. 2000;23(4):334–40.

Bradshaw C, Atkinson S, Doody O. Employing a qualitative description Approach in Health Care Research. Glob Qual Nurs Res. 2017;4:2333393617742282.

PubMed   PubMed Central   Google Scholar  

Bellone JM, Barner JC, Lopez DA. Postdischarge interventions by pharmacists and impact on hospital readmission rates. J Am Pharm Assoc (2003). 2012;52(3):358–62.

Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are Enough? Qual Health Res. 2016;27(4):591–608.

World Health Organization. Adherence to long-term therapies: evidence for action. 2003.

Fisher JD, Fisher WA, Amico KR, Harman JJ. An information-motivation-behavioral skills model of adherence to antiretroviral therapy. Health Psychol. 2006;25(4):462–73.

Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998;13(4):623–49.

ShiftEUROQOL Research FOndation EQ 5D Instruments. Accessed 30 July 2022 https://euroqol.org/eq-5d-instruments/sample-demo/

Jeppesen KM, Coyle JD, Miser WF. Screening questions to predict limited health literacy: a cross-sectional study of patients with diabetes mellitus. Ann Fam Med. 2009;7(1):24–31.

Giordano TP, Guzman D, Clark R, Charlebois ED, Bangsberg DR. Measuring adherence to antiretroviral therapy in a diverse population using a visual analogue scale. HIV Clin Trials. 2004;5(2):74–9.

Horne R, Weinman J, Hankins M. The beliefs about medicines questionnaire: the development and evaluation of a new method for assessing the cognitive representation of medication. Psychol Health. 1999;14(1):1–24.

Horne R, Chapman SC, Parham R, Freemantle N, Forbes A, Cooper V. Understanding patients’ adherence-related beliefs about medicines prescribed for long-term conditions: a meta-analytic review of the necessity-concerns Framework. PLoS ONE. 2013;8(12):e80633.

Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Res Psychol. 2006;3(2):77–101.

Waibel S, Henao D, Aller M-B, Vargas I, Vázquez M-L. What do we know about patients’ perceptions of continuity of care? A meta-synthesis of qualitative studies. Int J Qual Health Care. 2011;24(1):39–48.

Rognan SE, Jørgensen MJ, Mathiesen L, Druedahl LC, Lie HB, Bengtsson K, et al. The way you talk, do I have a choice?’ Patient narratives of medication decision-making during hospitalization. Int J Qualitative Stud Health Well-being. 2023;18(1):2250084.

Michel B, Hemery M, Rybarczyk-Vigouret MC, Wehrle P, Beck M. Drug-dispensing problems community pharmacists face when patients are discharged from hospitals: a study about 537 prescriptions in Alsace. Int J Qual Health Care. 2016;28(6):779–84.

Bruhwiler LD, Hersberger KE, Lutters M. Hospital discharge: what are the problems, information needs and objectives of community pharmacists? A mixed method approach. Pharm Pract (Granada). 2017;15(3):1046.

Knight DA, Thompson D, Mathie E, Dickinson A. Seamless care? Just a list would have helped!’ Older people and their carer’s experiences of support with medication on discharge home from hospital. Health Expect. 2013;16(3):277–91.

Gualandi R, Masella C, Viglione D, Tartaglini D. Exploring the hospital patient journey: what does the patient experience? PLoS ONE. 2019;14(12):e0224899.

Norberg H, Håkansson Lindqvist M, Gustafsson M. Older individuals’ experiences of Medication Management and Care after Discharge from Hospital: an interview study. Patient Prefer Adherence. 2023;17:781–92.

Jones KC, Austad K, Silver S, Cordova-Ramos EG, Fantasia KL, Perez DC, et al. Patient perspectives of the hospital discharge process: a qualitative study. J Patient Exp. 2023;10:23743735231171564.

Hesselink G, Flink M, Olsson M, Barach P, Dudzik-Urbaniak E, Orrego C, et al. Are patients discharged with care? A qualitative study of perceptions and experiences of patients, family members and care providers. BMJ Qual Saf. 2012;21(Suppl 1):i39–49.

Murray SA, Kendall M, Carduff E, Worth A, Harris FM, Lloyd A, et al. Use of serial qualitative interviews to understand patients’ evolving experiences and needs. BMJ. 2009;339:b3702.

Berger ZD, Boss EF, Beach MC. Communication behaviors and patient autonomy in hospital care: a qualitative study. Patient Educ Couns. 2017;100(8):1473–81.

Davis RE, Jacklin R, Sevdalis N, Vincent CA. Patient involvement in patient safety: what factors influence patient participation and engagement? Health Expect. 2007;10(3):259–67.

Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27(5):520–6.

Mitchell SE, Gardiner PM, Sadikova E, Martin JM, Jack BW, Hibbard JH, et al. Patient activation and 30-day post-discharge hospital utilization. J Gen Intern Med. 2014;29(2):349–55.

Weir DL, Motulsky A, Abrahamowicz M, Lee TC, Morgan S, Buckeridge DL, et al. Failure to follow medication changes made at hospital discharge is associated with adverse events in 30 days. Health Serv Res. 2020;55(4):512–23.

Kripalani S, Goggins K, Nwosu S, Schildcrout J, Mixon AS, McNaughton C, et al. Medication nonadherence before hospitalization for Acute Cardiac events. J Health Commun. 2015;20(Suppl 2):34–42.

Mortelmans L, De Baetselier E, Goossens E, Dilles T. What happens after Hospital Discharge? Deficiencies in Medication Management encountered by geriatric patients with polypharmacy. Int J Environ Res Public Health. 2021;18(13).

Mortelmans L, Goossens E, Dilles T. Beliefs about medication after hospital discharge in geriatric patients with polypharmacy. Geriatr Nurs. 2022;43:280–7.

Bandiera C, Ribaut J, Dima AL, Allemann SS, Molesworth K, Kalumiya K et al. Swiss Priority setting on implementing Medication Adherence interventions as Part of the European ENABLE COST action. Int J Public Health. 2022;67.

Elliott R, Boyd M, Nde S. at e. Supporting adherence for people starting a new medication for a long-term condition through community pharmacies: a pragmaticrandomised controlled trial of the New Medicine Service. 2015.

Grimmsmann T, Schwabe U, Himmel W. The influence of hospitalisation on drug prescription in primary care–a large-scale follow-up study. Eur J Clin Pharmacol. 2007;63(8):783–90.

Larsen MD, Rosholm JU, Hallas J. The influence of comprehensive geriatric assessment on drug therapy in elderly patients. Eur J Clin Pharmacol. 2014;70(2):233–9.

Viktil KK, Blix HS, Eek AK, Davies MN, Moger TA, Reikvam A. How are drug regimen changes during hospitalisation handled after discharge: a cohort study. BMJ Open. 2012;2(6):e001461.

Strehlau AG, Larsen MD, Søndergaard J, Almarsdóttir AB, Rosholm J-U. General practitioners’ continuation and acceptance of medication changes at sectorial transitions of geriatric patients - a qualitative interview study. BMC Fam Pract. 2018;19(1):168.

Anderson TS, Lee S, Jing B, Fung K, Ngo S, Silvestrini M, et al. Prevalence of diabetes medication intensifications in older adults discharged from US Veterans Health Administration Hospitals. JAMA Netw Open. 2020;3(3):e201511.

Royal Pharmaceutical Society. Keeping patients safewhen they transfer between care providers– getting the medicines right June 2012. Accessed 27 October 2023 https://www.rpharms.com/Portals/0/RPS%20document%20library/Open%20access/Publications/Keeping%20patients%20safe%20transfer%20of%20care%20report.pdf

International Pharmaceutical Federation (FIP). Medicines reconciliation: A toolkit for pharmacists. Accessed 23 September 2023 https://www.fip.org/file/4949

Californian Pharmacist Assiociation Transitions of Care Resource Guide. https://cdn.ymaws.com/www.cshp.org/resource/resmgr/Files/Practice-Policy/For_Pharmacists/transitions_of_care_final_10.pdf

Royal Collegue of Physicians. Medication safety at hospital discharge: Improvement guide and resource. Accessed 18 September 2023 https://www.rcplondon.ac.uk/file/33421/download

Douglas N, Campbell W, Hinckley J. Implementation science: buzzword or game changer. J Speech Lang Hear Res. 2015;58.

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Acknowledgements

The authors would like to thank all the patients who took part in this study. We would also like to thank the Geneva University Hospitals Patients Partners + 3P platform as well as Mrs. Tourane Corbière and Mr. Joël Mermoud, patient partners, who reviewed interview guides for clarity and significance. We would like to thank Samuel Fabbi, Vitcoryavarman Koh, and Pierre Repiton for the transcriptions of the audio recordings.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Léa Solh Dost & Marie P. Schneider

Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland

Division of Endocrinology, Diabetes, Hypertension and Nutrition, Department of Medicine, Geneva University Hospitals, Geneva, Switzerland

Giacomo Gastaldi

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Contributions

LS, GG, and MS conceptualized and designed the study. LS and GG screened and recruited participants. LS conducted the interviews. LS, GG, and MS performed data analysis and interpretation. LS drafted the manuscript and LS and MS worked on the different versions. MS and GG approved the final manuscript.

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Correspondence to Léa Solh Dost or Marie P. Schneider .

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Ethics approval was sought and granted by the Cantonal Research Ethics Commission, Geneva (CCER) (2020 − 01779), and informed consent to participate was obtained from all participants.

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Solh Dost, L., Gastaldi, G. & Schneider, M. Patient medication management, understanding and adherence during the transition from hospital to outpatient care - a qualitative longitudinal study in polymorbid patients with type 2 diabetes. BMC Health Serv Res 24 , 620 (2024). https://doi.org/10.1186/s12913-024-10784-9

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Received : 28 June 2023

Accepted : 26 February 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s12913-024-10784-9

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• Continuity of care
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