how to make sampling procedure in qualitative research

Chapter 5. Sampling

Introduction.

Most Americans will experience unemployment at some point in their lives. Sarah Damaske ( 2021 ) was interested in learning about how men and women experience unemployment differently. To answer this question, she interviewed unemployed people. After conducting a “pilot study” with twenty interviewees, she realized she was also interested in finding out how working-class and middle-class persons experienced unemployment differently. She found one hundred persons through local unemployment offices. She purposefully selected a roughly equal number of men and women and working-class and middle-class persons for the study. This would allow her to make the kinds of comparisons she was interested in. She further refined her selection of persons to interview:

I decided that I needed to be able to focus my attention on gender and class; therefore, I interviewed only people born between 1962 and 1987 (ages 28–52, the prime working and child-rearing years), those who worked full-time before their job loss, those who experienced an involuntary job loss during the past year, and those who did not lose a job for cause (e.g., were not fired because of their behavior at work). ( 244 )

The people she ultimately interviewed compose her sample. They represent (“sample”) the larger population of the involuntarily unemployed. This “theoretically informed stratified sampling design” allowed Damaske “to achieve relatively equal distribution of participation across gender and class,” but it came with some limitations. For one, the unemployment centers were located in primarily White areas of the country, so there were very few persons of color interviewed. Qualitative researchers must make these kinds of decisions all the time—who to include and who not to include. There is never an absolutely correct decision, as the choice is linked to the particular research question posed by the particular researcher, although some sampling choices are more compelling than others. In this case, Damaske made the choice to foreground both gender and class rather than compare all middle-class men and women or women of color from different class positions or just talk to White men. She leaves the door open for other researchers to sample differently. Because science is a collective enterprise, it is most likely someone will be inspired to conduct a similar study as Damaske’s but with an entirely different sample.

This chapter is all about sampling. After you have developed a research question and have a general idea of how you will collect data (observations or interviews), how do you go about actually finding people and sites to study? Although there is no “correct number” of people to interview, the sample should follow the research question and research design. You might remember studying sampling in a quantitative research course. Sampling is important here too, but it works a bit differently. Unlike quantitative research, qualitative research involves nonprobability sampling. This chapter explains why this is so and what qualities instead make a good sample for qualitative research.

Quick Terms Refresher

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.
• Sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
• Sample size is how many individuals (or units) are included in your sample.

The “Who” of Your Research Study

After you have turned your general research interest into an actual research question and identified an approach you want to take to answer that question, you will need to specify the people you will be interviewing or observing. In most qualitative research, the objects of your study will indeed be people. In some cases, however, your objects might be content left by people (e.g., diaries, yearbooks, photographs) or documents (official or unofficial) or even institutions (e.g., schools, medical centers) and locations (e.g., nation-states, cities). Chances are, whatever “people, places, or things” are the objects of your study, you will not really be able to talk to, observe, or follow every single individual/object of the entire population of interest. You will need to create a sample of the population . Sampling in qualitative research has different purposes and goals than sampling in quantitative research. Sampling in both allows you to say something of interest about a population without having to include the entire population in your sample.

We begin this chapter with the case of a population of interest composed of actual people. After we have a better understanding of populations and samples that involve real people, we’ll discuss sampling in other types of qualitative research, such as archival research, content analysis, and case studies. We’ll then move to a larger discussion about the difference between sampling in qualitative research generally versus quantitative research, then we’ll move on to the idea of “theoretical” generalizability, and finally, we’ll conclude with some practical tips on the correct “number” to include in one’s sample.

Sampling People

To help think through samples, let’s imagine we want to know more about “vaccine hesitancy.” We’ve all lived through 2020 and 2021, and we know that a sizable number of people in the United States (and elsewhere) were slow to accept vaccines, even when these were freely available. By some accounts, about one-third of Americans initially refused vaccination. Why is this so? Well, as I write this in the summer of 2021, we know that some people actively refused the vaccination, thinking it was harmful or part of a government plot. Others were simply lazy or dismissed the necessity. And still others were worried about harmful side effects. The general population of interest here (all adult Americans who were not vaccinated by August 2021) may be as many as eighty million people. We clearly cannot talk to all of them. So we will have to narrow the number to something manageable. How can we do this?

First, we have to think about our actual research question and the form of research we are conducting. I am going to begin with a quantitative research question. Quantitative research questions tend to be simpler to visualize, at least when we are first starting out doing social science research. So let us say we want to know what percentage of each kind of resistance is out there and how race or class or gender affects vaccine hesitancy. Again, we don’t have the ability to talk to everyone. But harnessing what we know about normal probability distributions (see quantitative methods for more on this), we can find this out through a sample that represents the general population. We can’t really address these particular questions if we only talk to White women who go to college with us. And if you are really trying to generalize the specific findings of your sample to the larger population, you will have to employ probability sampling , a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. Why randomly? If truly random, all the members have an equal opportunity to be a part of the sample, and thus we avoid the problem of having only our friends and neighbors (who may be very different from other people in the population) in the study. Mathematically, there is going to be a certain number that will be large enough to allow us to generalize our particular findings from our sample population to the population at large. It might surprise you how small that number can be. Election polls of no more than one thousand people are routinely used to predict actual election outcomes of millions of people. Below that number, however, you will not be able to make generalizations. Talking to five people at random is simply not enough people to predict a presidential election.

In order to answer quantitative research questions of causality, one must employ probability sampling. Quantitative researchers try to generalize their findings to a larger population. Samples are designed with that in mind. Qualitative researchers ask very different questions, though. Qualitative research questions are not about “how many” of a certain group do X (in this case, what percentage of the unvaccinated hesitate for concern about safety rather than reject vaccination on political grounds). Qualitative research employs nonprobability sampling . By definition, not everyone has an equal opportunity to be included in the sample. The researcher might select White women they go to college with to provide insight into racial and gender dynamics at play. Whatever is found by doing so will not be generalizable to everyone who has not been vaccinated, or even all White women who have not been vaccinated, or even all White women who have not been vaccinated who are in this particular college. That is not the point of qualitative research at all. This is a really important distinction, so I will repeat in bold: Qualitative researchers are not trying to statistically generalize specific findings to a larger population . They have not failed when their sample cannot be generalized, as that is not the point at all.

In the previous paragraph, I said it would be perfectly acceptable for a qualitative researcher to interview five White women with whom she goes to college about their vaccine hesitancy “to provide insight into racial and gender dynamics at play.” The key word here is “insight.” Rather than use a sample as a stand-in for the general population, as quantitative researchers do, the qualitative researcher uses the sample to gain insight into a process or phenomenon. The qualitative researcher is not going to be content with simply asking each of the women to state her reason for not being vaccinated and then draw conclusions that, because one in five of these women were concerned about their health, one in five of all people were also concerned about their health. That would be, frankly, a very poor study indeed. Rather, the qualitative researcher might sit down with each of the women and conduct a lengthy interview about what the vaccine means to her, why she is hesitant, how she manages her hesitancy (how she explains it to her friends), what she thinks about others who are unvaccinated, what she thinks of those who have been vaccinated, and what she knows or thinks she knows about COVID-19. The researcher might include specific interview questions about the college context, about their status as White women, about the political beliefs they hold about racism in the US, and about how their own political affiliations may or may not provide narrative scripts about “protective whiteness.” There are many interesting things to ask and learn about and many things to discover. Where a quantitative researcher begins with clear parameters to set their population and guide their sample selection process, the qualitative researcher is discovering new parameters, making it impossible to engage in probability sampling.

Looking at it this way, sampling for qualitative researchers needs to be more strategic. More theoretically informed. What persons can be interviewed or observed that would provide maximum insight into what is still unknown? In other words, qualitative researchers think through what cases they could learn the most from, and those are the cases selected to study: “What would be ‘bias’ in statistical sampling, and therefore a weakness, becomes intended focus in qualitative sampling, and therefore a strength. The logic and power of purposeful sampling like in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the inquiry, thus the term purposeful sampling” ( Patton 2002:230 ; emphases in the original).

Before selecting your sample, though, it is important to clearly identify the general population of interest. You need to know this before you can determine the sample. In our example case, it is “adult Americans who have not yet been vaccinated.” Depending on the specific qualitative research question, however, it might be “adult Americans who have been vaccinated for political reasons” or even “college students who have not been vaccinated.” What insights are you seeking? Do you want to know how politics is affecting vaccination? Or do you want to understand how people manage being an outlier in a particular setting (unvaccinated where vaccinations are heavily encouraged if not required)? More clearly stated, your population should align with your research question . Think back to the opening story about Damaske’s work studying the unemployed. She drew her sample narrowly to address the particular questions she was interested in pursuing. Knowing your questions or, at a minimum, why you are interested in the topic will allow you to draw the best sample possible to achieve insight.

Once you have your population in mind, how do you go about getting people to agree to be in your sample? In qualitative research, it is permissible to find people by convenience. Just ask for people who fit your sample criteria and see who shows up. Or reach out to friends and colleagues and see if they know anyone that fits. Don’t let the name convenience sampling mislead you; this is not exactly “easy,” and it is certainly a valid form of sampling in qualitative research. The more unknowns you have about what you will find, the more convenience sampling makes sense. If you don’t know how race or class or political affiliation might matter, and your population is unvaccinated college students, you can construct a sample of college students by placing an advertisement in the student paper or posting a flyer on a notice board. Whoever answers is your sample. That is what is meant by a convenience sample. A common variation of convenience sampling is snowball sampling . This is particularly useful if your target population is hard to find. Let’s say you posted a flyer about your study and only two college students responded. You could then ask those two students for referrals. They tell their friends, and those friends tell other friends, and, like a snowball, your sample gets bigger and bigger.

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

My early experience with qualitative research was rather unique. At that time, I needed to do a project that required me to interview first-generation college students, and my friends, with whom I had been sharing a dorm for two years, just perfectly fell into the sample category. Thus, I just asked them and easily “gained my access” to the research subject; I know them, we are friends, and I am part of them. I am an insider. I also thought, “Well, since I am part of the group, I can easily understand their language and norms, I can capture their honesty, read their nonverbal cues well, will get more information, as they will be more opened to me because they trust me.” All in all, easy access with rich information. But, gosh, I did not realize that my status as an insider came with a price! When structuring the interview questions, I began to realize that rather than focusing on the unique experiences of my friends, I mostly based the questions on my own experiences, assuming we have similar if not the same experiences. I began to struggle with my objectivity and even questioned my role; am I doing this as part of the group or as a researcher? I came to know later that my status as an insider or my “positionality” may impact my research. It not only shapes the process of data collection but might heavily influence my interpretation of the data. I came to realize that although my inside status came with a lot of benefits (especially for access), it could also bring some drawbacks.

—Dede Setiono, PhD student focusing on international development and environmental policy, Oregon State University

The more you know about what you might find, the more strategic you can be. If you wanted to compare how politically conservative and politically liberal college students explained their vaccine hesitancy, for example, you might construct a sample purposively, finding an equal number of both types of students so that you can make those comparisons in your analysis. This is what Damaske ( 2021 ) did. You could still use convenience or snowball sampling as a way of recruitment. Post a flyer at the conservative student club and then ask for referrals from the one student that agrees to be interviewed. As with convenience sampling, there are variations of purposive sampling as well as other names used (e.g., judgment, quota, stratified, criterion, theoretical). Try not to get bogged down in the nomenclature; instead, focus on identifying the general population that matches your research question and then using a sampling method that is most likely to provide insight, given the types of questions you have.

There are all kinds of ways of being strategic with sampling in qualitative research. Here are a few of my favorite techniques for maximizing insight:

• Consider using “extreme” or “deviant” cases. Maybe your college houses a prominent anti-vaxxer who has written about and demonstrated against the college’s policy on vaccines. You could learn a lot from that single case (depending on your research question, of course).
• Consider “intensity”: people and cases and circumstances where your questions are more likely to feature prominently (but not extremely or deviantly). For example, you could compare those who volunteer at local Republican and Democratic election headquarters during an election season in a study on why party matters. Those who volunteer are more likely to have something to say than those who are more apathetic.
• Maximize variation, as with the case of “politically liberal” versus “politically conservative,” or include an array of social locations (young vs. old; Northwest vs. Southeast region). This kind of heterogeneity sampling can capture and describe the central themes that cut across the variations: any common patterns that emerge, even in this wildly mismatched sample, are probably important to note!
• Rather than maximize the variation, you could select a small homogenous sample to describe some particular subgroup in depth. Focus groups are often the best form of data collection for homogeneity sampling.
• Think about which cases are “critical” or politically important—ones that “if it happens here, it would happen anywhere” or a case that is politically sensitive, as with the single “blue” (Democratic) county in a “red” (Republican) state. In both, you are choosing a site that would yield the most information and have the greatest impact on the development of knowledge.
• On the other hand, sometimes you want to select the “typical”—the typical college student, for example. You are trying to not generalize from the typical but illustrate aspects that may be typical of this case or group. When selecting for typicality, be clear with yourself about why the typical matches your research questions (and who might be excluded or marginalized in doing so).
• Finally, it is often a good idea to look for disconfirming cases : if you are at the stage where you have a hypothesis (of sorts), you might select those who do not fit your hypothesis—you will surely learn something important there. They may be “exceptions that prove the rule” or exceptions that force you to alter your findings in order to make sense of these additional cases.

In addition to all these sampling variations, there is the theoretical approach taken by grounded theorists in which the researcher samples comparative people (or events) on the basis of their potential to represent important theoretical constructs. The sample, one can say, is by definition representative of the phenomenon of interest. It accompanies the constant comparative method of analysis. In the words of the funders of Grounded Theory , “Theoretical sampling is sampling on the basis of the emerging concepts, with the aim being to explore the dimensional range or varied conditions along which the properties of the concepts vary” ( Strauss and Corbin 1998:73 ).

When Your Population is Not Composed of People

I think it is easiest for most people to think of populations and samples in terms of people, but sometimes our units of analysis are not actually people. They could be places or institutions. Even so, you might still want to talk to people or observe the actions of people to understand those places or institutions. Or not! In the case of content analyses (see chapter 17), you won’t even have people involved at all but rather documents or films or photographs or news clippings. Everything we have covered about sampling applies to other units of analysis too. Let’s work through some examples.

Case Studies

When constructing a case study, it is helpful to think of your cases as sample populations in the same way that we considered people above. If, for example, you are comparing campus climates for diversity, your overall population may be “four-year college campuses in the US,” and from there you might decide to study three college campuses as your sample. Which three? Will you use purposeful sampling (perhaps [1] selecting three colleges in Oregon that are different sizes or [2] selecting three colleges across the US located in different political cultures or [3] varying the three colleges by racial makeup of the student body)? Or will you select three colleges at random, out of convenience? There are justifiable reasons for all approaches.

As with people, there are different ways of maximizing insight in your sample selection. Think about the following rationales: typical, diverse, extreme, deviant, influential, crucial, or even embodying a particular “pathway” ( Gerring 2008 ). When choosing a case or particular research site, Rubin ( 2021 ) suggests you bear in mind, first, what you are leaving out by selecting this particular case/site; second, what you might be overemphasizing by studying this case/site and not another; and, finally, whether you truly need to worry about either of those things—“that is, what are the sources of bias and how bad are they for what you are trying to do?” ( 89 ).

Once you have selected your cases, you may still want to include interviews with specific people or observations at particular sites within those cases. Then you go through possible sampling approaches all over again to determine which people will be contacted.

Content: Documents, Narrative Accounts, And So On

Although not often discussed as sampling, your selection of documents and other units to use in various content/historical analyses is subject to similar considerations. When you are asking quantitative-type questions (percentages and proportionalities of a general population), you will want to follow probabilistic sampling. For example, I created a random sample of accounts posted on the website studentloanjustice.org to delineate the types of problems people were having with student debt ( Hurst 2007 ). Even though my data was qualitative (narratives of student debt), I was actually asking a quantitative-type research question, so it was important that my sample was representative of the larger population (debtors who posted on the website). On the other hand, when you are asking qualitative-type questions, the selection process should be very different. In that case, use nonprobabilistic techniques, either convenience (where you are really new to this data and do not have the ability to set comparative criteria or even know what a deviant case would be) or some variant of purposive sampling. Let’s say you were interested in the visual representation of women in media published in the 1950s. You could select a national magazine like Time for a “typical” representation (and for its convenience, as all issues are freely available on the web and easy to search). Or you could compare one magazine known for its feminist content versus one antifeminist. The point is, sample selection is important even when you are not interviewing or observing people.

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

We have already discussed some of the differences in the goals of quantitative and qualitative sampling above, but it is worth further discussion. The quantitative researcher seeks a sample that is representative of the population of interest so that they may properly generalize the results (e.g., if 80 percent of first-gen students in the sample were concerned with costs of college, then we can say there is a strong likelihood that 80 percent of first-gen students nationally are concerned with costs of college). The qualitative researcher does not seek to generalize in this way . They may want a representative sample because they are interested in typical responses or behaviors of the population of interest, but they may very well not want a representative sample at all. They might want an “extreme” or deviant case to highlight what could go wrong with a particular situation, or maybe they want to examine just one case as a way of understanding what elements might be of interest in further research. When thinking of your sample, you will have to know why you are selecting the units, and this relates back to your research question or sets of questions. It has nothing to do with having a representative sample to generalize results. You may be tempted—or it may be suggested to you by a quantitatively minded member of your committee—to create as large and representative a sample as you possibly can to earn credibility from quantitative researchers. Ignore this temptation or suggestion. The only thing you should be considering is what sample will best bring insight into the questions guiding your research. This has implications for the number of people (or units) in your study as well, which is the topic of the next section.

What is the Correct “Number” to Sample?

Because we are not trying to create a generalizable representative sample, the guidelines for the “number” of people to interview or news stories to code are also a bit more nebulous. There are some brilliant insightful studies out there with an n of 1 (meaning one person or one account used as the entire set of data). This is particularly so in the case of autoethnography, a variation of ethnographic research that uses the researcher’s own subject position and experiences as the basis of data collection and analysis. But it is true for all forms of qualitative research. There are no hard-and-fast rules here. The number to include is what is relevant and insightful to your particular study.

That said, humans do not thrive well under such ambiguity, and there are a few helpful suggestions that can be made. First, many qualitative researchers talk about “saturation” as the end point for data collection. You stop adding participants when you are no longer getting any new information (or so very little that the cost of adding another interview subject or spending another day in the field exceeds any likely benefits to the research). The term saturation was first used here by Glaser and Strauss ( 1967 ), the founders of Grounded Theory. Here is their explanation: “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 [or she] sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. [They go] out of [their] 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” ( 61 ).

It makes sense that the term was developed by grounded theorists, since this approach is rather more open-ended than other approaches used by qualitative researchers. With so much left open, having a guideline of “stop collecting data when you don’t find anything new” is reasonable. However, saturation can’t help much when first setting out your sample. How do you know how many people to contact to interview? What number will you put down in your institutional review board (IRB) protocol (see chapter 8)? You may guess how many people or units it will take to reach saturation, but there really is no way to know in advance. The best you can do is think about your population and your questions and look at what others have done with similar populations and questions.

Here are some suggestions to use as a starting point: For phenomenological studies, try to interview at least ten people for each major category or group of people . If you are comparing male-identified, female-identified, and gender-neutral college students in a study on gender regimes in social clubs, that means you might want to design a sample of thirty students, ten from each group. This is the minimum suggested number. Damaske’s ( 2021 ) sample of one hundred allows room for up to twenty-five participants in each of four “buckets” (e.g., working-class*female, working-class*male, middle-class*female, middle-class*male). If there is more than one comparative group (e.g., you are comparing students attending three different colleges, and you are comparing White and Black students in each), you can sometimes reduce the number for each group in your sample to five for, in this case, thirty total students. But that is really a bare minimum you will want to go. A lot of people will not trust you with only “five” cases in a bucket. Lareau ( 2021:24 ) advises a minimum of seven or nine for each bucket (or “cell,” in her words). The point is to think about what your analyses might look like and how comfortable you will be with a certain number of persons fitting each category.

Because qualitative research takes so much time and effort, it is rare for a beginning researcher to include more than thirty to fifty people or units in the study. You may not be able to conduct all the comparisons you might want simply because you cannot manage a larger sample. In that case, the limits of who you can reach or what you can include may influence you to rethink an original overcomplicated research design. Rather than include students from every racial group on a campus, for example, you might want to sample strategically, thinking about the most contrast (insightful), possibly excluding majority-race (White) students entirely, and simply using previous literature to fill in gaps in our understanding. For example, one of my former students was interested in discovering how race and class worked at a predominantly White institution (PWI). Due to time constraints, she simplified her study from an original sample frame of middle-class and working-class domestic Black and international African students (four buckets) to a sample frame of domestic Black and international African students (two buckets), allowing the complexities of class to come through individual accounts rather than from part of the sample frame. She wisely decided not to include White students in the sample, as her focus was on how minoritized students navigated the PWI. She was able to successfully complete her project and develop insights from the data with fewer than twenty interviewees. [1]

But what if you had unlimited time and resources? Would it always be better to interview more people or include more accounts, documents, and units of analysis? No! Your sample size should reflect your research question and the goals you have set yourself. Larger numbers can sometimes work against your goals. If, for example, you want to help bring out individual stories of success against the odds, adding more people to the analysis can end up drowning out those individual stories. Sometimes, the perfect size really is one (or three, or five). It really depends on what you are trying to discover and achieve in your study. Furthermore, studies of one hundred or more (people, documents, accounts, etc.) can sometimes be mistaken for quantitative research. Inevitably, the large sample size will push the researcher into simplifying the data numerically. And readers will begin to expect generalizability from such a large sample.

To summarize, “There are no rules for sample size in qualitative inquiry. Sample size depends on what you want to know, the purpose of the inquiry, what’s at stake, what will be useful, what will have credibility, and what can be done with available time and resources” ( Patton 2002:244 ).

How did you find/construct a sample?

Since qualitative researchers work with comparatively small sample sizes, getting your sample right is rather important. Yet it is also difficult to accomplish. For instance, a key question you need to ask yourself is whether you want a homogeneous or heterogeneous sample. In other words, do you want to include people in your study who are by and large the same, or do you want to have diversity in your sample?

For many years, I have studied the experiences of students who were the first in their families to attend university. There is a rather large number of sampling decisions I need to consider before starting the study. (1) Should I only talk to first-in-family students, or should I have a comparison group of students who are not first-in-family? (2) Do I need to strive for a gender distribution that matches undergraduate enrollment patterns? (3) Should I include participants that reflect diversity in gender identity and sexuality? (4) How about racial diversity? First-in-family status is strongly related to some ethnic or racial identity. (5) And how about areas of study?

As you can see, if I wanted to accommodate all these differences and get enough study participants in each category, I would quickly end up with a sample size of hundreds, which is not feasible in most qualitative research. In the end, for me, the most important decision was to maximize the voices of first-in-family students, which meant that I only included them in my sample. As for the other categories, I figured it was going to be hard enough to find first-in-family students, so I started recruiting with an open mind and an understanding that I may have to accept a lack of gender, sexuality, or racial diversity and then not be able to say anything about these issues. But I would definitely be able to speak about the experiences of being first-in-family.

—Wolfgang Lehmann, author of “Habitus Transformation and Hidden Injuries”

Examples of “Sample” Sections in Journal Articles

Think about some of the studies you have read in college, especially those with rich stories and accounts about people’s lives. Do you know how the people were selected to be the focus of those stories? If the account was published by an academic press (e.g., University of California Press or Princeton University Press) or in an academic journal, chances are that the author included a description of their sample selection. You can usually find these in a methodological appendix (book) or a section on “research methods” (article).

Here are two examples from recent books and one example from a recent article:

Example 1 . In It’s Not like I’m Poor: How Working Families Make Ends Meet in a Post-welfare World , the research team employed a mixed methods approach to understand how parents use the earned income tax credit, a refundable tax credit designed to provide relief for low- to moderate-income working people ( Halpern-Meekin et al. 2015 ). At the end of their book, their first appendix is “Introduction to Boston and the Research Project.” After describing the context of the study, they include the following description of their sample selection:

In June 2007, we drew 120 names at random from the roughly 332 surveys we gathered between February and April. Within each racial and ethnic group, we aimed for one-third married couples with children and two-thirds unmarried parents. We sent each of these families a letter informing them of the opportunity to participate in the in-depth portion of our study and then began calling the home and cell phone numbers they provided us on the surveys and knocking on the doors of the addresses they provided.…In the end, we interviewed 115 of the 120 families originally selected for the in-depth interview sample (the remaining five families declined to participate). ( 22 )

Was their sample selection based on convenience or purpose? Why do you think it was important for them to tell you that five families declined to be interviewed? There is actually a trick here, as the names were pulled randomly from a survey whose sample design was probabilistic. Why is this important to know? What can we say about the representativeness or the uniqueness of whatever findings are reported here?

Example 2 . In When Diversity Drops , Park ( 2013 ) examines the impact of decreasing campus diversity on the lives of college students. She does this through a case study of one student club, the InterVarsity Christian Fellowship (IVCF), at one university (“California University,” a pseudonym). Here is her description:

I supplemented participant observation with individual in-depth interviews with sixty IVCF associates, including thirty-four current students, eight former and current staff members, eleven alumni, and seven regional or national staff members. The racial/ethnic breakdown was twenty-five Asian Americans (41.6 percent), one Armenian (1.6 percent), twelve people who were black (20.0 percent), eight Latino/as (13.3 percent), three South Asian Americans (5.0 percent), and eleven people who were white (18.3 percent). Twenty-nine were men, and thirty-one were women. Looking back, I note that the higher number of Asian Americans reflected both the group’s racial/ethnic composition and my relative ease about approaching them for interviews. ( 156 )

How can you tell this is a convenience sample? What else do you note about the sample selection from this description?

Example 3. The last example is taken from an article published in the journal Research in Higher Education . Published articles tend to be more formal than books, at least when it comes to the presentation of qualitative research. In this article, Lawson ( 2021 ) is seeking to understand why female-identified college students drop out of majors that are dominated by male-identified students (e.g., engineering, computer science, music theory). Here is the entire relevant section of the article:

Method Participants Data were collected as part of a larger study designed to better understand the daily experiences of women in MDMs [male-dominated majors].…Participants included 120 students from a midsize, Midwestern University. This sample included 40 women and 40 men from MDMs—defined as any major where at least 2/3 of students are men at both the university and nationally—and 40 women from GNMs—defined as any may where 40–60% of students are women at both the university and nationally.… Procedure A multi-faceted approach was used to recruit participants; participants were sent targeted emails (obtained based on participants’ reported gender and major listings), campus-wide emails sent through the University’s Communication Center, flyers, and in-class presentations. Recruitment materials stated that the research focused on the daily experiences of college students, including classroom experiences, stressors, positive experiences, departmental contexts, and career aspirations. Interested participants were directed to email the study coordinator to verify eligibility (at least 18 years old, man/woman in MDM or woman in GNM, access to a smartphone). Sixteen interested individuals were not eligible for the study due to the gender/major combination. ( 482ff .)

What method of sample selection was used by Lawson? Why is it important to define “MDM” at the outset? How does this definition relate to sampling? Why were interested participants directed to the study coordinator to verify eligibility?

Final Words

I have found that students often find it difficult to be specific enough when defining and choosing their sample. It might help to think about your sample design and sample recruitment like a cookbook. You want all the details there so that someone else can pick up your study and conduct it as you intended. That person could be yourself, but this analogy might work better if you have someone else in mind. When I am writing down recipes, I often think of my sister and try to convey the details she would need to duplicate the dish. We share a grandmother whose recipes are full of handwritten notes in the margins, in spidery ink, that tell us what bowl to use when or where things could go wrong. Describe your sample clearly, convey the steps required accurately, and then add any other details that will help keep you on track and remind you why you have chosen to limit possible interviewees to those of a certain age or class or location. Imagine actually going out and getting your sample (making your dish). Do you have all the necessary details to get started?

Table 5.1. Sampling Type and Strategies

Further Readings

Fusch, Patricia I., and Lawrence R. Ness. 2015. “Are We There Yet? Data Saturation in Qualitative Research.” Qualitative Report 20(9):1408–1416.

Saunders, Benjamin, Julius Sim, Tom Kinstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. “Saturation in Qualitative Research: Exploring Its Conceptualization and Operationalization.”  Quality & Quantity  52(4):1893–1907.

• Rubin ( 2021 ) suggests a minimum of twenty interviews (but safer with thirty) for an interview-based study and a minimum of three to six months in the field for ethnographic studies. For a content-based study, she suggests between five hundred and one thousand documents, although some will be “very small” ( 243–244 ). ↵

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

The actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).  Sampling frames can differ from the larger population when specific exclusions are inherent, as in the case of pulling names randomly from voter registration rolls where not everyone is a registered voter.  This difference in frame and population can undercut the generalizability of quantitative results.

The specific group of individuals that you will collect data from.  Contrast population.

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A sampling strategy in which the sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the sample.  This is often done through a lottery or other chance mechanisms (e.g., a random selection of every twelfth name on an alphabetical list of voters).  Also known as random sampling .

The selection of research participants or other data sources based on availability or accessibility, in contrast to purposive sampling .

A sample generated non-randomly by asking participants to help recruit more participants the idea being that a person who fits your sampling criteria probably knows other people with similar criteria.

Broad codes that are assigned to the main issues emerging in the data; identifying themes is often part of initial coding . 

A form of case selection focusing on examples that do not fit the emerging patterns. This allows the researcher to evaluate rival explanations or to define the limitations of their research findings. While disconfirming cases are found (not sought out), researchers should expand their analysis or rethink their theories to include/explain them.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

The result of probability sampling, in which a sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the random sample.  This is often done through a lottery or other chance mechanisms (e.g., the random selection of every twelfth name on an alphabetical list of voters).  This is typically not required in qualitative research but rather essential for the generalizability of quantitative research.

A form of case selection or purposeful sampling in which cases that are unusual or special in some way are chosen to highlight processes or to illuminate gaps in our knowledge of a phenomenon.   See also extreme case .

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

The accuracy with which results or findings can be transferred to situations or people other than those originally studied.  Qualitative studies generally are unable to use (and are uninterested in) statistical generalizability where the sample population is said to be able to predict or stand in for a larger population of interest.  Instead, qualitative researchers often discuss “theoretical generalizability,” in which the findings of a particular study can shed light on processes and mechanisms that may be at play in other settings.  See also statistical generalization and theoretical generalization .

A term used by IRBs to denote all materials aimed at recruiting participants into a research study (including printed advertisements, scripts, audio or video tapes, or websites).  Copies of this material are required in research protocols submitted to IRB.

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

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

• Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling.
• Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results.
• It’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique for your qualitative research.

Qualitative research seeks to understand social phenomena from the perspective of those experiencing them. It involves collecting non-numerical data such as interviews, observations, and written documents to gain insights into human experiences, attitudes, and behaviors. While qualitative research can provide rich and nuanced insights, the accuracy and generalizability of findings depend on the quality of the sampling process. Sampling techniques are a critical component of qualitative research as it involves selecting a group of participants who can provide valuable insights into the research questions.

This article explores different types of sampling techniques in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques in qualitative research. and then compare and contrast these techniques to provide guidance on choosing the most appropriate method for a particular study. Additionally, you’ll find best practices for sampling and learn about ethical considerations researchers need to consider in selecting a sample. Overall, this article aims to help researchers conduct effective and high-quality sampling in qualitative research.

In this Article:

• Purposive Sampling
• Convenience Sampling
• Snowball Sampling
• Theoretical Sampling

Factors to Consider When Choosing a Sampling Technique

Practical approaches to sampling: recommended practices, final thoughts, get expert guidance on your sample needs.

Want expert input on the best sampling technique for your qualitative research project? Book a consultation for trusted advice.

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4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We’ll break down the definition of each technique, when to use it, and its advantages and disadvantages.

1. Purposive Sampling

Purposive sampling, or judgmental sampling, is a non-probability sampling technique in qualitative research that’s commonly used. In purposive sampling, researchers intentionally select participants with specific characteristics or unique experiences related to the research question. The goal is to identify and recruit participants who can provide rich and diverse data to enhance the research findings.

Purposive sampling is used when researchers seek to identify individuals or groups with particular knowledge, skills, or experiences relevant to the research question. For instance, in a study examining the experiences of cancer patients undergoing chemotherapy, purposive sampling may be used to recruit participants who have undergone chemotherapy in the past year. Researchers can better understand the phenomenon under investigation by selecting individuals with relevant backgrounds.

Purposive Sampling: Strengths and Weaknesses

Purposive sampling is a powerful tool for researchers seeking to select participants who can provide valuable insight into their research question. This method is advantageous when studying groups with technical characteristics or experiences where a random selection of participants may yield different results.

One of the main advantages of purposive sampling is the ability to improve the quality and accuracy of data collected by selecting participants most relevant to the research question. This approach also enables researchers to collect data from diverse participants with unique perspectives and experiences related to the research question.

However, researchers should also be aware of potential bias when using purposive sampling. The researcher’s judgment may influence the selection of participants, resulting in a biased sample that does not accurately represent the broader population. Another disadvantage is that purposive sampling may not be representative of the more general population, which limits the generalizability of the findings. To guarantee the accuracy and dependability of data obtained through purposive sampling, researchers must provide a clear and transparent justification of their selection criteria and sampling approach. This entails outlining the specific characteristics or experiences required for participants to be included in the study and explaining the rationale behind these criteria. This level of transparency not only helps readers to evaluate the validity of the findings, but also enhances the replicability of the research.

2. Convenience Sampling  

When time and resources are limited, researchers may opt for convenience sampling as a quick and cost-effective way to recruit participants. In this non-probability sampling technique, participants are selected based on their accessibility and willingness to participate rather than their suitability for the research question. Qualitative research often uses this approach to generate various perspectives and experiences.

During the COVID-19 pandemic, convenience sampling was a valuable method for researchers to collect data quickly and efficiently from participants who were easily accessible and willing to participate. For example, in a study examining the experiences of university students during the pandemic, convenience sampling allowed researchers to recruit students who were available and willing to share their experiences quickly. While the pandemic may be over, convenience sampling during this time highlights its value in urgent situations where time and resources are limited.

Convenience Sampling: Strengths and Weaknesses

Convenience sampling offers several advantages to researchers, including its ease of implementation and cost-effectiveness. This technique allows researchers to quickly and efficiently recruit participants without spending time and resources identifying and contacting potential participants. Furthermore, convenience sampling can result in a diverse pool of participants, as individuals from various backgrounds and experiences may be more likely to participate.

While convenience sampling has the advantage of being efficient, researchers need to acknowledge its limitations. One of the primary drawbacks of convenience sampling is that it is susceptible to selection bias. Participants who are more easily accessible may not be representative of the broader population, which can limit the generalizability of the findings. Furthermore, convenience sampling may lead to issues with the reliability of the results, as it may not be possible to replicate the study using the same sample or a similar one.

To mitigate these limitations, researchers should carefully define the population of interest and ensure the sample is drawn from that population. For instance, if a study is investigating the experiences of individuals with a particular medical condition, researchers can recruit participants from specialized clinics or support groups for that condition. Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.

3. Snowball Sampling

Snowball sampling, also called referral sampling, is a unique approach researchers use to recruit participants in qualitative research. The technique involves identifying a few initial participants who meet the eligibility criteria and asking them to refer others they know who also fit the requirements. The sample size grows as referrals are added, creating a chain-like structure.

Snowball sampling enables researchers to reach out to individuals who may be hard to locate through traditional sampling methods, such as members of marginalized or hidden communities. For instance, in a study examining the experiences of undocumented immigrants, snowball sampling may be used to identify and recruit participants through referrals from other undocumented immigrants.

Snowball Sampling: Strengths and Weaknesses

Snowball sampling can produce in-depth and detailed data from participants with common characteristics or experiences. Since referrals are made within a network of individuals who share similarities, researchers can gain deep insights into a specific group’s attitudes, behaviors, and perspectives.

4. Theoretical Sampling

Theoretical sampling is a sophisticated and strategic technique that can help researchers develop more in-depth and nuanced theories from their data. Instead of selecting participants based on convenience or accessibility, researchers using theoretical sampling choose participants based on their potential to contribute to the emerging themes and concepts in the data. This approach allows researchers to refine their research question and theory based on the data they collect rather than forcing their data to fit a preconceived idea.

Theoretical sampling is used when researchers conduct grounded theory research and have developed an initial theory or conceptual framework. In a study examining cancer survivors’ experiences, for example, theoretical sampling may be used to identify and recruit participants who can provide new insights into the coping strategies of survivors.

Theoretical Sampling: Strengths and Weaknesses

One of the significant advantages of theoretical sampling is that it allows researchers to refine their research question and theory based on emerging data. This means the research can be highly targeted and focused, leading to a deeper understanding of the phenomenon being studied. Additionally, theoretical sampling can generate rich and in-depth data, as participants are selected based on their potential to provide new insights into the research question.

Participants are selected based on their perceived ability to offer new perspectives on the research question. This means specific perspectives or experiences may be overrepresented in the sample, leading to an incomplete understanding of the phenomenon being studied. Additionally, theoretical sampling can be time-consuming and resource-intensive, as researchers must continuously analyze the data and recruit new participants.

To mitigate the potential for bias, researchers can take several steps. One way to reduce bias is to use a diverse team of researchers to analyze the data and make participant selection decisions. Having multiple perspectives and backgrounds can help prevent researchers from unconsciously selecting participants who fit their preconceived notions or biases.

Another solution would be to use reflexive sampling. Reflexive sampling involves selecting participants aware of the research process and provides insights into how their biases and experiences may influence their perspectives. By including participants who are reflexive about their subjectivity, researchers can generate more nuanced and self-aware findings.

Choosing the proper sampling technique in qualitative research is one of the most critical decisions a researcher makes when conducting a study. The preferred method can significantly impact the accuracy and reliability of the research results.

For instance, purposive sampling provides a more targeted and specific sample, which helps to answer research questions related to that particular population or phenomenon. However, this approach may also introduce bias by limiting the diversity of the sample.

Conversely, convenience sampling may offer a more diverse sample regarding demographics and backgrounds but may also introduce bias by selecting more willing or available participants.

Snowball sampling may help study hard-to-reach populations, but it can also limit the sample’s diversity as participants are selected based on their connections to existing participants.

Theoretical sampling may offer an opportunity to refine the research question and theory based on emerging data, but it can also be time-consuming and resource-intensive.

Additionally, the choice of sampling technique can impact the generalizability of the research findings. Therefore, it’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique. By doing so, researchers can select the most appropriate method for their research question and ensure the validity and reliability of their findings.

Tips for Selecting Participants

When selecting participants for a qualitative research study, it is crucial to consider the research question and the purpose of the study. In addition, researchers should identify the specific characteristics or criteria they seek in their sample and select participants accordingly.

One helpful tip for selecting participants is to use a pre-screening process to ensure potential participants meet the criteria for inclusion in the study. Another technique is using multiple recruitment methods to ensure the sample is diverse and representative of the studied population.

Ensuring Diversity in Samples

Diversity in the sample is important to ensure the study’s findings apply to a wide range of individuals and situations. One way to ensure diversity is to use stratified sampling, which involves dividing the population into subgroups and selecting participants from each subset. This helps establish that the sample is representative of the larger population.

Maintaining Ethical Considerations

When selecting participants for a qualitative research study, it is essential to ensure ethical considerations are taken into account. Researchers must ensure participants are fully informed about the study and provide their voluntary consent to participate. They must also ensure participants understand their rights and that their confidentiality and privacy will be protected.

A qualitative research study’s success hinges on its sampling technique’s effectiveness. The choice of sampling technique must be guided by the research question, the population being studied, and the purpose of the study. Whether purposive, convenience, snowball, or theoretical sampling, the primary goal is to ensure the validity and reliability of the study’s findings.

By thoughtfully weighing the pros and cons of each sampling technique in qualitative research, researchers can make informed decisions that lead to more reliable and accurate results. In conclusion, carefully selecting a sampling technique is integral to the success of a qualitative research study, and a thorough understanding of the available options can make all the difference in achieving high-quality research outcomes.

If you’re interested in improving your research and sampling methods, Sago offers a variety of solutions. Our qualitative research platforms, such as QualBoard and QualMeeting, can assist you in conducting research studies with precision and efficiency. Our robust global panel and recruitment options help you reach the right people. We also offer qualitative and quantitative research services to meet your research needs. Contact us today to learn more about how we can help improve your research outcomes.

Find the Right Sample for Your Qualitative Research

Trust our team to recruit the participants you need using the appropriate techniques. Book a consultation with our team to get started .

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Sampling Techniques for Qualitative Research

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• Heather Douglas 4  

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This chapter explains how to design suitable sampling strategies for qualitative research. The focus of this chapter is purposive (or theoretical) sampling to produce credible and trustworthy explanations of a phenomenon (a specific aspect of society). A specific research question (RQ) guides the methodology (the study design or approach ). It defines the participants, location, and actions to be used to answer the question. Qualitative studies use specific tools and techniques ( methods ) to sample people, organizations, or whatever is to be examined. The methodology guides the selection of tools and techniques for sampling, data analysis, quality assurance, etc. These all vary according to the purpose and design of the study and the RQ. In this chapter, a fake example is used to demonstrate how to apply your sampling strategy in a developing country.

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Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research, the role of sampling in mixed methods-research.

Preparation of Qualitative Research

Douglas, H. (2010). Divergent orientations in social entrepreneurship organisations. In K. Hockerts, J. Robinson, & J. Mair (Eds.), Values and opportunities in social entrepreneurship (pp. 71–95). Palgrave Macmillan.

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Douglas, H. (2022). Sampling Techniques for Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_29

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• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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10.2 Sampling in qualitative research

Learning objectives.

• Define nonprobability sampling, and describe instances in which a researcher might choose a nonprobability sampling technique
• Describe the different types of nonprobability samples

Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of whatever phenomenon it is that they are studying. In this section, we’ll examine the techniques that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most likely to use in their work.

Nonprobability sampling

Nonprobability sampling refers to sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know with nonprobability samples whether a sample is truly representative of a larger population. But that’s okay. Generalizing to a larger population is not the goal with nonprobability samples or qualitative research. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind (that would mean committing one of the errors of informal inquiry discussed in Chapter 1). We’ll take a closer look at the process of selecting research elements when drawing a nonprobability sample. But first, let’s consider why a researcher might choose to use a nonprobability sample.

When are nonprobability samples ideal? One instance might be when we’re starting a big research project. For example, if we’re conducting survey research, we may want to administer a draft of our survey to a few people who seem to resemble the folks we’re interested in studying in order to help work out kinks in the survey. We might also use a nonprobability sample if we’re conducting a pilot study or some exploratory research. This can be a quick way to gather some initial data and help us get some idea of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples can be useful for setting up, framing, or beginning research, even quantitative research. But it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques. Researchers also use nonprobability samples in full-blown research projects. These projects are usually qualitative in nature, where the researcher’s goal is in-depth, idiographic understanding rather than more general, nomothetic understanding.

Types of nonprobability samples

There are several types of nonprobability samples that researchers use. These include purposive samples, snowball samples, quota samples, and convenience samples. While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research, and because they are both nonprobability methods, we include them in this section of the chapter.

To draw a purposive sample , a researcher selects participants from their sampling frame because they have characteristics that the researcher desires. A researcher begins with specific characteristics in mind that she wishes to examine and then seeks out research participants who cover that full range of characteristics. For example, if you are studying mental health supports on your campus, you may want to be sure to include not only students, but mental health practitioners and student affairs administrators. You might also select students who currently use mental health supports, those who dropped out of supports, and those who are waiting to receive supports. The purposive part of purposive sampling comes from selecting specific participants on purpose because you already know they have characteristics—being an administrator, dropping out of mental health supports—that you need in your sample.

Note that these are different than inclusion criteria, which are more general requirements a person must possess to be a part of your sample. For example, one of the inclusion criteria for a study of your campus’ mental health supports might be that participants had to have visited the mental health center in the past year. That is different than purposive sampling. In purposive sampling, you know characteristics of individuals and recruit them because of those characteristics. For example, I might recruit Jane because she stopped seeking supports this month, JD because she has worked at the center for many years, and so forth.

Also, it’s important to recognize that purposive sampling requires you to have prior information about your participants before recruiting them because you need to know their perspectives or experiences before you know whether you want them in your sample. This is a common mistake that many students make. What I often hear is, “I’m using purposive sampling because I’m recruiting people from the health center,” or something like that. That’s not purposive sampling. Purposive sampling is recruiting specific people because of the various characteristics and perspectives they bring to your sample. Imagine we were creating a focus group. A purposive sample might gather clinicians, patients, administrators, staff, and former patients together so they can talk as a group. Purposive sampling would seek out people that have each of those attributes.

Quota sampling is another nonprobability sampling strategy that takes purposive sampling one step further. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation. Subgroups are created based on each category, and the researcher decides how many people to include from each subgroup and collects data from that number for each subgroup. Let’s consider a study of student satisfaction with on-campus housing. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves and instead eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. It is possible as well that your review of literature on the topic suggests that campus housing experiences vary by gender. If that is that case, perhaps you’ll decide on four important subgroups: men who live in apartments, women who live in apartments, men who live in dorm rooms, and women who live in dorm rooms. Your quota sample would include five people from each of the four subgroups.

In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. The leading polling entity at the time, The Literary Digest, predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide, but Gallup’s polling disagreed. Gallup successfully predicted Roosevelt’s win and subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election.  [1] Among other problems, the fact that Gallup’s quota categories did not represent those who actually voted (Neuman, 2007)  [2] underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods.  [3] While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings. For that, you need probability sampling, which we will discuss in the next section.

Qualitative researchers can also use snowball sampling techniques to identify study participants. In snowball sampling , a researcher identifies one or two people she’d like to include in her study but then relies on those initial participants to help identify additional study participants. Thus, the researcher’s sample builds and becomes larger as the study continues, much as a snowball builds and becomes larger as it rolls through the snow. Snowball sampling is an especially useful strategy when a researcher wishes to study a stigmatized group or behavior. For example, a researcher who wanted to study how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting a call for interviewees in the newspaper or making an announcement about the study at some large social gathering. Instead, the researcher might know someone with the condition, interview that person, and ask the person to refer others they may know with the genital herpes to contact you to participate in the study. Having a previous participant vouch for the researcher may help new potential participants feel more comfortable about being included in the study.

Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. In addition to using this sampling strategy for potentially stigmatized populations, it is also a useful strategy to use when the researcher’s group of interest is likely to be difficult to find, not only because of some stigma associated with the group, but also because the group may be relatively rare. This was the case for Steven Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011)  [4] who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers first relied on their own networks to identify study participants, but because members of the study’s target population were not easy to find, access to the networks of initial study participants was very important for identifying additional participants. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received $50 for participating in the study and an additional $20 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

Finally, convenience sampling is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from those people or other relevant elements to which she has most convenient access. This method, also sometimes referred to as availability sampling, is most useful in exploratory research or in student projects in which probability sampling is too costly or difficult. If you’ve ever been interviewed by a fellow student for a class project, you have likely been a part of a convenience sample. While convenience samples offer one major benefit—convenience—they do not offer the rigor needed to make conclusions about larger populations. That is the subject of our next section on probability sampling.

Key Takeaways

• Nonprobability samples might be used when researchers are conducting qualitative (or idiographic) research, exploratory research, student projects, or pilot studies.
• There are several types of nonprobability samples including purposive samples, snowball samples, quota samples, and convenience samples.
• Convenience sample- researcher gathers data from whatever cases happen to be convenient
• Nonprobability sampling- sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown
• Purposive sample- when a researcher seeks out participants with specific characteristics
• Quota sample- when a researcher selects cases from within several different subgroups
• Snowball sample- when a researcher relies on participant referrals to recruit new participants

Image attributions

business by helpsg CC-0

network by geralt CC-0

• For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “The first measured century” at http://www.pbs.org/fmc/timeline/e1948election.htm. ↵
• Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson. ↵
• If you are interested in the history of polling, I recommend reading Fried, A. (2011). Pathways to polling: Crisis, cooperation, and the making of public opinion professions . New York, NY: Routledge. ↵
• Kogan, S. M., Wejnert, C., Chen, Y., Brody, G. H., & Slater, L. M. (2011). Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. Journal of Adolescent Research , 26 , 30–60. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

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

How to conduct qualitative research?

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

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

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

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

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

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

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• Open access
• Published: 24 July 2024

Mental health preparedness and response to epidemics focusing on COVID-19 pandemic: a qualitative study in Iran

• Khadijeh Akbari 1 , 2 ,
• Armin Zareiyan 3 ,
• Arezoo Yari 4 , 5 ,
• Mehdi Najafi 6 ,
• Maryam Azizi 7 &
• Abbas Ostadtaghizadeh 1  

BMC Public Health volume  24 , Article number:  1980 ( 2024 ) Cite this article

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Metrics details

During epidemics, the number of individuals whose mental health is affected is greater than those affected by the infection itself. This is because psychological factors have a direct relationship with the primary causes of the disease and mortality worldwide. Therefore, an increasing investment in research and strategic actions for mental health is essential globally, given the prevalence of infectious diseases.

The aim of this study was to elucidate and describe the strategies for mental health preparedness and response during epidemics, with a focus on the COVID-19 pandemic in Iran.

A qualitative study was conducted in Iran from 2022 to 2023. Purposeful Sampling was employed, continuing until data saturation was achieved. Data collection involved semi-structured interviews and observational notes with 20 managers and experts possessing expertise, experience, and knowledge in mental health. Ultimately, the participants' opinions, based on their experiences, were analyzed using the qualitative content analysis method with a conventional approach, resulting in the categorization of data into codes, subcategories, and categories.

The study revealed participants' opinions and experiences, categorized into two overarching categories: Preparedness, Policy-Making, and Planning Strategies (with four subcategories), and Response Strategies (comprising thirteen subcategories).

The opinions and experiences of managers and experts in this study revealed that an appropriate mental health response during pandemics requires preparedness before the occurrence of such crises and the implementation of suitable response strategies after the occurrence. Managers, policymakers, and decision-makers in this field should pay attention to the solutions derived from the experiences of such crises to respond more preparedly in the future.

Peer Review reports

Mental health is an essential component of human well-being that can be significantly impacted during pandemics and epidemics [ 1 ]. Communicable diseases, especially those requiring isolation and quarantine, pose substantial risks to individuals' mental health. Anxiety, stress, depression, and grief are common mental health issues experienced during and after the outbreak of a disease [ 2 ]. During the Ebola virus epidemic in West Africa during 2014–2016, significant mental health challenges were reported among the affected population [ 3 ]. The recent emergence of the COVID-19 pandemic has created conditions that have exacerbated many factors that can weaken mental health. Before 2020, mental disorders were among the leading contributors to the global burden of disease, with depression and anxiety disorders being the primary factors [ 4 ]. During the first year of the COVID-19 pandemic, the global prevalence of these disorders increased by 25% [ 5 ]. As observed in previous studies on other severe epidemics, the overall impact of a pandemic on mental health is not transient and is likely to persist for a long time even after the pandemic has ended [ 6 ]. Unpredictability [ 7 ], lack of preparedness, inconsistencies in guidelines, quarantines, containment strategies, unemployment, financial losses, physical distancing, isolation, chaos, uncertainty [ 8 ], ease of access to communication strategies and transmission of sensational misinformation and disinformation [ 9 ] are among the factors that lead to increased emotional distress, anxiety, and depression [ 8 ].

While health emergencies have been a recurring aspect of human history, the global community found itself unprepared for the impact of the COVID-19 pandemic [ 7 , 10 ]. According to a World Health Organization survey, the pandemic disrupted or halted mental health services in 93% of the world's countries, coinciding with a rising demand for mental health support [ 11 ]. The longstanding prevalence of mental illnesses has posed a persistent public health challenge. In the United States, the provision of high-quality healthcare services faces formidable challenges attributed to various gaps in the mental health care system. These gaps include disparities in treatment, elevated drug prices, fragmented systems, ineffective policies, structural issues, workforce shortages, limited access, and financial barriers. The urgency to address these gaps has intensified, particularly in light of the escalating mental health issues stemming from the COVID-19 pandemic [ 8 ].

The sudden outbreak of public health crises always presents significant challenges for the mental health care system. Effective management of communicable disease pandemics such as COVID-19 requires approaches that encompass various aspects affecting outcomes. Timely provision of mental health care during epidemics is crucial, with interventions tailored to different stages of the epidemic, including during and after the outbreak [ 12 ]. Policy decisions should prioritize reinforcing community-based care, providing support, enhancing capacity for public mental health research, ensuring easy access to healthcare services [ 8 ], and effectively supporting healthcare providers.As highlighted in Irandoost et al. [ 13 ], understanding the experiences, challenges, and adaptation strategies of healthcare providers is essential for improving mental health response during such crises [ 13 ].

The Islamic Republic of Iran has accumulated significant experience in providing social and psychological support during disasters over the past two decades. For example, in response to the inevitable mental health consequences of the COVID-19 pandemic, various measures were implemented nationwide. At the onset of the virus outbreak, the predominant feelings among the population were anxiety, worry, and confusion. The Ministry of Health and Medical Education (MOH) initiated several coping strategies to address stress and tension in society. These strategies included training healthcare workers in primary healthcare systems and establishing a helpline to increase access to care. Collaboration with other governmental sectors, such as the Islamic Republic of Iran Broadcasting (IRIB) and social media networks, was employed to ensure the efficient dissemination of information. Lessons learned from managing mental health issues during emergencies, particularly amid the COVID-19 pandemic, emphasize the importance of adopting a dynamic approach to address community mental health needs effectively [ 14 ].

Public mental health efforts aim to enhance disaster response [ 7 ] and integrate mental health interventions into global preparedness and response programs [ 15 ], informed by scientific evidence and cultural considerations. Further research is required to develop psychological interventions for improving mental health outcomes [ 16 ]. Qualitative studies, by exploring perspectives and experiences, facilitate a deeper understanding of phenomena. Our qualitative approach captured nuanced experiences and response strategies during the epidemics, providing insights not easily uncovered through quantitative methods. This study highlights the challenges and response strategies during the COVID-19 pandemic in Iran. The insights gained can guide future policies and interventions for similar crises. The researchers' experience in mental health and disaster response emphasizes the importance of integrating mental health support into preparedness plans and adopting a dynamic approach to community mental health needs during and after epidemics. The aim of this study was to elucidate and describe the strategies for mental health preparedness and response to epidemics focusing on the COVID-19 pandemic in Iran.

Materials and methods

A qualitative study was conducted employing the content analysis method to elucidate and describe strategies and programs related to mental health preparedness and response to epidemics, with a focus on the COVID-19 pandemic in Iran during 2022–2023. Content analysis is a systematic method aimed at achieving depth and breadth in describing phenomena, leading to valid interpretations of information and the generation of new insights. It is particularly suitable for exploring individuals' experiences and perspectives on specific topics. In this study, conventional content analysis was used, wherein categories are derived concurrently with the analysis of interview text, allowing researchers to gain a better understanding of the phenomenon under study [ 17 ].

Participants and setting

To ensure the maxim of variation, participant selection was purposeful, aiming to include individuals with diverse experiences and expertise relevant to the primary phenomenon or key concepts under investigation. The study encompassed managers, experts, and individuals with experience in mental health during epidemics and the COVID-19 pandemic, representing various academic and executive environments within responsible organizations. These organizations included the Ministry of Health, Social Welfare Organization, Psychiatric and Psychological Scientific Associations, municipalities, Iranian Red Crescent Society, National Disaster Management Organization, universities, and private sector entities. The exclusion criterion of unwillingness to participate helped maintain the integrity of the sample.

Coordination with potential participants was established through telephone communication, and their inclusion in the study was contingent on their willingness to participate. Selection criteria were based on knowledge, expertise, experience, and organizational affiliations, ensuring participants played active roles in policymaking, decision-making, service delivery, implementation, monitoring, and supervision in the mental health field. Additionally, input from academics was sought due to their valuable insights derived from studies in this field. Data collection continued until theoretical data saturation was achieved, indicating that no further data could be obtained. Deliberate selection of participants with varying opinions ensured diversity in the sample.

To ensure transparency and mitigate potential conflicts of interest during the interviews, participants were explicitly informed that their responses would be treated confidentially and would not impact their professional roles or affiliations. Furthermore, efforts were made to maintain impartiality throughout the interview process, emphasizing that their input would solely contribute to research findings and not influence their work. Informed consent was obtained from all participants as part of the research process. Privacy of information (including names, interview recordings, and transcripts) was strictly maintained, and coding was used instead of names to ensure confidentiality. Participants had the right to withdraw from the study at any time, and the option to share the results upon request was provided to them.

Data collection

To collect the data, coordination and pre-scheduled appointments were made either in the workplace of the participants or virtually, and in-depth individual interviews were conducted with them. The semi-structured interviews were conducted using an interview guide. Initially, two unstructured interviews were conducted to determine the main interview outline and complete the interview guide questions. All interviews were conducted individually to ensure that participants could freely share their experiences and perspectives without influence from others.

At the beginning of the interviews, participants were provided with an explanation of the study's objectives. The interviews commenced in a friendly environment with several general questions, such as introducing themselves. They were then asked if they had experienced responding to mental health issues during the COVID-19 pandemic. If a participant had relevant experience, they were requested to describe it. Subsequently, the interviews continued with the following main questions:

How would you describe your positive and negative experiences in this field? In order to provide mental health response strategies, what issues and challenges have you encountered? Please describe them. What solutions do you employ to deal with these challenges? Please give an example. Based on your experiences during the COVID-19 pandemic, describe the actions that the healthcare system should take in response to mental health issues during epidemics.

All interviews were audio-recorded with permission. Furthermore, the researcher (KH.A.) took notes during the interviews to gather the data more comprehensively. Data analysis was conducted simultaneously with data collection.

Data analysis

The data were analyzed using the qualitative content analysis method proposed by Graneheim and Lundman [ 18 ]. At the end of each interview session, in the first phase, the recorded interviews were listened to several times, and transcripts were created, incorporating verbatim accounts. Prior to analysis, in the second phase, the transcripts were read multiple times to familiarize the researchers (KH.A. and A.Y.) with the interviews. In the third phase, to the analyze the interviews, the transcripts were broken down into the smallest meaningful units and codes. These initial codes were then compared with each other, and similar codes were categorized into subcategories. Furthermore, by continually comparing the subcategories and based on their relevance and similarity, these subcategories were placed within the main categories, which contained the main themes and were somewhat abstract. To confirm the codes, the text was read multiple times (KH.A. and A.Y.). A third researcher (A.O.T.), with higher academic and executive expertise, refined the codes and categories in the final stage. Content analysis was performed on the data written in the Persian language before translation manually.

Trustworthiness

The trustworthiness of a qualitative research study relies on the rigor of the methodology [ 18 ]. Four criteria for evaluating qualitative research are credibility, transferability, dependability, and confirmability [ 19 ].

For credibility, the researcher ensured trustworthiness and acceptability of the data through long-term and continuous involvement with the environment and participants, allocating an average duration of 10 months to data collection and analysis. All interview transcripts and analysis stages were reviewed by two expert individuals experienced in qualitative research, who provided initial coding. A third researcher refined the codes and categories in the final stage, incorporating supplementary comments. Additionally, interview transcripts were returned to some participants for feedback, with corrections made accordingly.

"Dependability" refers to the stability and reliability of data over time and under similar conditions. To assess dependability, an external audit was conducted by an individual with expertise in the field. Specifically, an external auditor with a PhD in health psychology, familiar with disaster-related research, reviewed the data to ensure consistency and reliability in its interpretation.

"Transferability" in qualitative research refers to how the findings of a study can be applied to other contexts or populations. To enhance transferability in this research, the researcher employed various strategies, including simultaneous data collection and analysis, ensuring coherence between research questions and methods, comparing results with other studies, providing a step-by-step report of each research stage, and involving a diverse spectrum of participants. This approach ensures that the findings are relevant to a broader audience beyond the specific study environment.

"Confirmability," which relates to the accuracy of all stages of research and the transparency of the research method, signifies the need to ensure that the findings are derived from the data and not influenced by the researcher's biases or preconceptions. In this study, rigorous measures were taken to maintain the accuracy and transparency of all research stages. Detailed documentation was conducted, encompassing the processes of data collection and analysis, as well as the researcher's notes and interpretations. This meticulous record-keeping aimed to provide interested readers with the ability to align the study with their own contexts and utilize it effectively.

In this study, a total of 20 participants took part, comprising 8 females (40%) and 12 males (60%). The mean age of the participants was 48.5 years, ranging from 35 to 64 years. Educational backgrounds varied, with one participant holding a master's degree and one possessing a postdoctoral degree. The remaining participants were medical specialists with doctoral degrees (Table  1 ). Due to geographical distances, interviews with three participants were conducted virtually. The interviews lasted 45 to 60 min, with an average duration of 52 min. All interviews were conducted in Persian. The study was conducted between August 2021 and June 2022.

By analyzing the participants' interviews, their experiences were categorized into two main categories and 17 sub categories (Table 2 ).

Policy-making, planning, and preparedness

This category encompassed all the actions that need to be taken in advance to reduce damage and ensure necessary preparedness in the field of mental health during epidemics. This main category composes four subcategories as follows:

Mental health governance

The participants identified the governance of mental health as one of the effective strategies for an appropriate response. With mental health governance, the goals of the organization and the appropriate structure for achieving those goals, along with relevant laws and regulations, are established. Mental health should govern all levels and dimensions of the health-related organizations, and the impact of psychological factors on individual and societal health should be recognized as a priority. For example, participant number 5, a health psychologist, stated:

"Absolutely, prioritizing mental health in health policies and programs is crucial for overall well-being. Raising awareness about the risks of neglecting mental health among relevant authorities is essential. Collaboration between the Ministry of Health and other agencies, along with the utilization of economic, social, and other resources, can help in effectively governing mental health. It's important to work together to ensure the mental well-being of individuals in our society."

By incorporating these insights, organizations can develop comprehensive strategies that not only address immediate mental health needs but also build resilience against future epidemics. This collaborative approach ensures that mental health remains a central focus in public health planning and response efforts.

Policy-making and laws

Developing transparent policies and laws for mental health during epidemics is considered a strategy for reducing damage and preparing for an appropriate response. The participants emphasized the need for clear policies. Participant number 11, a clinical psychologist, stated: "There should be transparent policies in the field of mental health during health crises, and this issue should be promoted in all health policies of the country, and clear laws should be formulated."

These policies should outline specific protocols and responsibilities for various stakeholders, ensuring a coordinated and efficient response. By having clear, established laws, mental health interventions can be systematically implemented, and resources can be allocated more effectively, ultimately enhancing the overall resilience of the healthcare system during epidemics.

The participants emphasized the importance of planning for an appropriate response during the preparedness phase as one of the crucial elements. Participant number 4, a specialist in health in disasters and emergencies, expressed the following:

"A comprehensive response plan should be considered before the outbreak of communicable diseases. Given the limitations of resources, planning should be done with a full understanding of capacities and infrastructure. Preparedness actions should be taken to provide an appropriate response during epidemics."

Proper planning ensures that mental health services are seamlessly integrated into the overall health response, addressing both immediate and long-term needs. This proactive approach helps mitigate the impact of the epidemic on mental health, providing structured and timely support to affected individuals and communities.

Training and exercise

Training and exercising preparedness programs before the occurrence of major epidemics are essential for an appropriate response, and they were among the proposed solutions by the participants. Participant number 15, a Psychiatrist, stated:

"Training of personnel and conducting exercises for mental health programs before the outbreak of communicable diseases is necessary so that all responsive organizations can function effectively. Training and practical exercises should be updated to maintain a high level of preparedness for personnel and organizations."

By engaging in continuous training and exercises, organizations can ensure that their teams are well-prepared to handle the psychological impacts of epidemics, providing timely and effective support to those in need. This proactive approach helps build resilience and ensures that mental health responses are integrated seamlessly into the broader epidemic response efforts.

Response strategies

This category represented the solutions provided by the participants for an appropriate mental health response during epidemics, including:

Command and leadership

Participants expressed the essentials of commanding in responding to emergency situations. The presence of unified leadership is crucial for integrated management, providing a framework for various organizations to work effectively together and synchronize their actions. Participant number 1, a specialist in health in disasters and emergencies, expressed:

"Unified command and leadership are necessary during the response phase. The Ministry of Health should be the leading authority for mental health in our country, and all collaborating organizations should operate under its leadership, ensuring sufficient authority. The incident command structure should be activated during the response phase to ensure coordination among all organizations."

Effective command and leadership not only streamline the decision-making process but also ensure that all organizations involved in the response are aligned in their efforts, thereby enhancing the overall effectiveness of the mental health response during epidemics.

Human resources

The participants identified the human resources management and organization as the most critical principle for an appropriate response. They emphasized the importance of supporting responders during the response phase. Participant number 6, a clinical psychologist, stated: "Healthcare workers have been under difficult conditions, with heavy and continuous work shifts, restrictions on interactions with family and friends, and concerns about contracting and transmitting the virus, causing them to endure significant pressure. Taking measures to ensure an adequate workforce for service delivery, appropriate work shifts, and providing equipment to maintain the safety of employees are crucial. Managers should attend to the various physical and psychological needs of responders."

Ensuring the wellbeing of healthcare workers not only helps in maintaining their efficiency but also prevents burnout, thereby improving the overall response to mental health challenges during epidemics.

Financial resources

Provision and organization of financial resources were highlighted as essential elements of the response strategies, emphasizing the need for provision of financial resources and their equitable distribution. Participant number 20, a specialist in health in disasters and emergencies, stated:

"In the realm of mental health, given its costliness, there is a need for financial provision and investment. To achieve maximum efficiency with minimal harm, there must be equity in allocating both large and small-scale resources." Moreover, Participant number 10, as a health psychologist, said:

"It is essential to take seriously the provision of insurance coverage for mental health services in the country, ensuring that it leads to a reduced financial burden on the public. Additionally, supporting the private sector, which provides mental health services, and addressing their concerns are also crucial." Ensuring that financial resources are adequately provided and equitably distributed is vital for maintaining the continuity and quality of mental health services, especially during crises.

Infrastructures

The participants emphasized the need to strengthen and enhance the existing infrastructure of community mental health for the provision of mental health services during the response phase. Participant number 2, a psychiatric nurse, expressed:

"Increasing capacity is crucial during epidemic response. Non-governmental organizations (NGOs) possess significant capacities at the grassroots level. These capacities can be utilized for educating the public and leveraging their capabilities and resources in times of response."

Moreover, Participant number 6, a clinical psychiatric, said: "It is essential to utilize and strengthen the existing infrastructure for delivering the intended services, and there is no need to create new structures. For instance, considering the valuable presence of the Primary Health Care (PHC) structure in our country, it is necessary to strengthen mental health services in primary healthcare."

Utilizing and enhancing existing infrastructures ensures a more efficient and coordinated response to mental health needs during epidemics, leveraging already established systems and resources. This approach maximizes the effectiveness of response efforts by utilizing the capacities already present within organizations and communities.

Monitoring and research

The participants emphasized the significance of both conducting research and monitoring mental health across all phases—pre-epidemic, epidemic, and post-epidemic—as crucial response strategies, including the establishment of centralized monitoring centers and the utilization of electronic health records. These measures can facilitate real-time monitoring of mental health indicators and trends, allowing for timely interventions and adjustments in response strategies. Participant number 13, a Psychiatrist, highlighted the necessity of robust mental health surveillance systems, stressing the importance of effective monitoring mechanisms. He stated:

"It is necessary to strengthen mental health surveillance systems for various societal groups by the Ministry of Health. These systems should include robust mechanisms for monitoring mental health indicators and trends, such as electronic health records and centralized monitoring centers. With an awareness of the mental health status of individuals in society and the factors influencing it, more informed decisions can be made for mental health responses during epidemics. Additionally, the evaluation of implemented programs and the ability to make adjustments if necessary are essential components of effective monitoring." Furthermore, Participant number 14, an emergency medicine specialist, stated:

"Epidemics have various effects on the mental health of the population. Extensive research should be conducted in this area, focusing on effective interventions for the mental health of the population and their effectiveness… Existing knowledge should be transferred and applied."

Investing in robust monitoring and research frameworks ensures ongoing assessment and adaptation of mental health responses across different phases of epidemics. By leveraging centralized monitoring centers and electronic health records, timely interventions can be implemented, addressing emerging mental health trends effectively.

Collaboration and coordination

The responsible participation of all sectors and coordinated utilization of all capacities in mental health response during epidemics were highlighted as crucial strategies by the participants. Participant number 3, a health psychologist, expressed:

"All sectors involved in mental health response, including the government, supporting groups, NGOs, Farzanegan Foundation, retirement homes, etc., should engage and participate. However, this participation of various organizations should not lead to conflicts. Coordination needs to be strengthened at a macro level, and contradictions in decision-making should be resolved. Sometimes, different decisions were made in managing the COVID-19 pandemic in our country, causing confusion and concern among the people."

Ensuring effective collaboration and coordination among diverse sectors is crucial to avoid conflicts and enhance decision-making coherence during epidemic responses.

Identification of vulnerable groups and individuals

The participants believed that one of the most important actions is to pay attention to vulnerable groups. For example, participant number 17, a specialist in health in disasters and emergencies, stated:

"Resources and necessary infrastructure should be considered to support vulnerable groups. Identifying and tracing vulnerable and at-risk groups for service provision and follow-up is crucial. We should strive to provide automated services to these segments. For example, we currently have a significant number of bereaved families who have lost their loved ones. Do we have any plans in place for them?"

Ensuring targeted support and services for vulnerable populations can enhance epidemic response effectiveness .

Mental health services

The participants emphasized that providing mental health services is one of the most crucial actions in the response phase. They underscored the necessity for delivering services that are safe, affordable, and timely. Participant number 7, a psychiatrist, stated:

"In the response phase, mental health services should be upgraded and expanded in terms of personnel, equipment, and space to increase public access to mental health services. Initial psychological aid should be provided, and services must be tailored to meet specific needs. New intervention models should be developed, and spaces for simply listening to people's concerns should be established. Free counseling centers within the community should be expanded. The privacy of individuals should be preserved and a sense of security should be ensured in receiving services ." Ensuring the affordability and accessibility of mental health services is crucial to meet the diverse needs of the population during epidemics.

Communication and information management

Timely and accurate communication and information management are the key solutions that contribute to maintaining mental health. Participant number 9, a specialist in health in disasters and emergencies, stated:

"Providing accurate and timely information is crucial during epidemics for the mental health of the community. Offering timely and accurate information and raising awareness among the public helps reduce the psychological impact of receiving various rumors. During an epidemic, combating misinformation is also vital, and it requires proper and effective communication. For example, we observe that misinformation leads to public confusion and prompts deviation from health guidelines, such as wearing masks or seeking appropriate treatments. At times, vaccine hesitancy due to misinformation has been evident." Furthermore, Participant number 19, an infectious disease specialist, said:

"A unified electronic mental health information system aids in mental health management. There should be a mechanism for accessing the required data, and the information from mental health records at health centers should be integrated and consolidated. Accurate data and statistics assist us in providing better responses." Ensuring secure and efficient communication channels during epidemics is essential for disseminating accurate information and countering misinformation effectively.

Public education and cultural promotion

Participants considered education and cultural initiatives as essential for preserving and enhancing mental health during the response phase. Participant number 8, a Clinical Psychologist, stated:

"One important solution is to provide empowerment educational packages to the public. Psychological training should be given to teach people how to take care of themselves. Providing education on lifestyle skills, self-care, self-awareness-based training, problem-solving, and emotional management to the general public is beneficial. Essentially, people should be informed about what has happened, what reactions exist, what the future holds, and where to access services."

Education and cultural promotion play a crucial role in fostering resilience and understanding among the population during epidemics, promoting proactive mental health practices and reducing stigma associated with seeking help.

Employment of technology, tools, and tele-psychiatry

The utilization of technology in various dimensions of mental health during the response phase to epidemics of communicable diseases was identified as highly practical and essential. Participant number 12, a health Psychologist , stated:

"The use of technology in the field of mental health during the COVID-19 pandemic was strongly emphasized, and it is necessary to strengthen telemedicine in the field of mental health (screening, treatment, etc.). Remote psychological interventions using the Internet and smart phones will help us to provide better responses in the future. For example, by utilizing these technologies, we can create support groups, facilitate discussions, and provide an outlet for individuals to express their feelings."

Leveraging technology, including telemedicine and digital platforms, enhances accessibility to mental health services, supports remote psychological interventions, and fosters community engagement and support during epidemics.

Social support

Paying attention to social support and gaining the trust and participation of the public were mentioned as effective response strategies. Participant number 16, a Clinical Psychologist, stated:

"Responsible engagement of the public significantly contributes to improving the mental health of the society during epidemics. It is essential to strengthen the spirit of social solidarity and, through sincere interactions, build their trust." Fostering social support networks and promoting community solidarity are crucial for enhancing mental well-being and resilience during epidemics.

Economic support

Financial and economic support for individuals affected by pandemics was also among the proposed solutions by the participants. Participant number 18, a Clinical Psychologist, stated:

"Supporting individuals who lose their jobs or face economic hardships due to pandemics should be a priority, as job and financial insecurities can have negative impacts on mental health."

Ensuring adequate economic support for affected individuals is essential for mitigating the psychological impact of economic uncertainties during pandemics.

Numerous solutions exist for mental health preparedness and response during pandemics. However, among studies related to this topic, a few have provided a comprehensive examination of these solutions. Through content analysis of the results of this study, a relatively comprehensive set of effective strategies in enhancing mental health preparedness and response during epidemics was compiled from the perspectives of individuals with experience in participating in this domain (Fig. 1 ).

Participants' experiences of key strategies for mental health preparedness and response during epidemics

Policy-making, planning, and preparedness strategies

The present study delved into policy-making, planning, and preparedness strategies for mental health, underscoring their critical importance prior to major epidemics to ensure readiness for an effective response.

In this study, we emphasized that mental health should consistently be a top priority for national health-related organizations. Mental health governance is highlighted not only as a fundamental human right but also essential for societal well-being, given the significant global burden of mental health disorders [ 8 ]. Recommendations stress the need for strong leadership and governance in mental health, involving active participation from public health advocates and governments to develop comprehensive policies and response programs [ 6 , 8 ]. Effective governance, planning, supervision, and accountability are crucial for achieving organizational goals [ 20 ]. The Iranian Constitution guarantees healthcare rights for all citizens, and the national mental health program, initiated in 1986 and expanded since, illustrates ongoing efforts to integrate mental health services nationwide [ 21 ]. Although attention has been paid to mental health strategies in Iran's national disaster preparedness programs in the recent years, it is necessary to pay attention to specific solutions in the pandemics and to implement them. It is suggested to develop a pandemic specific mental health preparedness plan based on the national disaster preparedness plan.

To address mental health preparedness and response during pandemics like COVID-19, participants stressed the importance of policymaking and legislation. Previous research has also emphasized the need for comprehensive policies and legal frameworks [ 22 ]. These include interventions such as national mental health support plans, increased political commitment, and strategies tailored to pandemic-induced mental health impacts [ 23 , 24 ]. Furthermore, there is consensus on the necessity of prioritizing mental health in epidemic responses. This involves enhancing mental health services, research, education, and allocating more budgets [ 25 ]. For instance, McCartan et al. [ 26 ] highlighted the importance of policy responses for mental health improvement, stressing the need for further research [ 26 ]. However, challenges remain in effectively implementing mental health policies during pandemics. More research is necessary to assess policy efficacy and identify areas for improvement. Comparative analyses with studies from other regions can offer valuable insights into best practices and strategies for addressing these challenges.

In our study, planning emerged as a crucial strategy for effective response before major epidemics. Healthcare systems bear the responsibility of planning for emergency response [ 27 , 28 ]. However, many lack comprehensive plans to enhance capacity and deliver healthcare services during emergencies [ 27 ]. This resonates with prior research emphasizing proactive planning for epidemics and disasters [ 29 ], alongside implementing prevention programs and advocating for tailored initiatives [ 6 ]. Integrating mental health interventions into public health preparedness and emergency response plans is essential for addressing epidemics effectively [ 15 ]. This study suggests that although mental health programs have been integrated into Iran's primary health care system, it is necessary to prepare the primary health care system to provide comprehensive mental health services in pandemics through a proactive approach.

This study showed that it is necessary to invest more in education and training to provide mental health services in pandemics. The study participants emphasized training and exercise as vital components of preparedness programs for mental health response during epidemics. This aligns with existing literature highlighting the significance of disaster preparedness in reducing community harm [ 30 ]. Training and exercise are recognized as essential aspects of disaster preparedness efforts [ 31 ]. Similarities exist between managing epidemics and other natural disasters, underscoring the importance of drafting emergency scenarios, implementing preventive measures, and conducting training and drills. Furthermore, engaging efficient human resources, promoting public participation, and employing Community-Based Disaster Risk Management (CBDRM) principles are critical [ 27 ]. The World Health Organization (WHO) advocates for prioritizing healthcare professional training for disaster response as a national and local priority, irrespective of a country's disaster experience [ 32 ]. Incorporating real-life scenarios into training programs is also essential for effective education and learning [ 33 ]. Our findings resonate with previous studies, emphasizing the importance of training and exercise in disaster preparedness for effective mental health response during epidemics.

Another noteworthy discovery in our study pertains to the strategies associated with the response phase of mental health during epidemics.

The results showed that although in Iran the united commanding of health is under the responsibility of the Ministry of Health, in practice, various organizations operate without coordination with national policies and programs. Implementing different programs with different approaches will not only not help people, but will cause them more confusion and anxiety. Participants highlighted the necessity of a unified and centralized command structure during epidemic response. This initial response phase necessitates the establishment of a robust command and control system to minimize uncertainty by efficiently processing information and mitigating unknown variables [ 34 ]. The response strategies adopted by different countries or regions are significantly influenced by their priorities and contextual conditions [ 27 ]. Notably, the implementation of an incident command system (ICS) has proven effective in managing the COVID-19 pandemic, facilitating improved communication, resource allocation, and overall safety measures [ 35 ]. The ICS framework aims to streamline command and control operations for swift and effective disaster response. Cook [ 36 ] demonstrated the efficacy of ICS implementation during the COVID-19 crisis, emphasizing its ability to mobilize personnel, assess situations, and develop comprehensive response plans. Despite its limitations, ICS provides a structured approach for disaster planning and response [ 36 ]. The examination of previous experiences, particularly with COVID-19 in Iran, sheds light on the relevance and effectiveness of the incident command system (ICS) as proposed by our contributors [ 37 ]. Evaluating the applicability and efficiency of ICS in the current context is crucial, considering its potential role in managing future epidemics and disasters. Iran's experience in COVID-19 showed that the monitoring, supervision and accountability of judicial institutions can play an effective role in the implementation of the incident management system by the Ministry of Health.

The high volume of providing mental health services during the pandemic usually is beyond the capacity of the existing mental health human resources. This causes them to experience fatigue and premature burnout. Lack of problem solving skills and mental resilience in these people and emotional actions aggravate this problem. It means that during pandemics like COVID-19, effective management of human resources is crucial for mental health response strategies. Participants emphasized the importance of supporting responders, addressing their psychological and educational needs, and mobilizing adequate personnel during the response phase. Studies have highlighted the heightened stress, fatigue, and psychological distress among healthcare workers due to increased workloads and risks during the COVID-19 pandemic [ 25 , 38 ]. Consequently, there is a strong emphasis on providing social and psychological support for frontline workers, including targeted psychosocial support programs [ 39 ]. Recommendations from a study by Jahanmehr and colleagues (2022) underscore the necessity of planning for psychological counseling and providing welfare facilities to alleviate psychological pressures on healthcare staff [ 40 ]. Training programs aimed at enhancing healthcare workers' mental health knowledge and skills are also essential [ 41 ]. Furthermore, strategies such as strengthening the mental health workforce, optimizing roles, and maximizing existing resources are critical components identified in the literature [ 22 , 29 ]. This study suggests that it is necessary to mobilize other human resources for this action in addition to strengthening the mental health workers' abilities.

It is clear that for the implementation of a program, it is necessary to allocate funds and financial resources. In addition, insurance coverage of mental health services is one of the programs that should be considered. Participants in this study stressed the critical need for financial resources, emphasizing that allocating funds to cover mental health expenses is essential during epidemics. The World Health Organization suggests that countries respond by increasing budgets and enhancing personnel capacity for mental health services amid the COVID-19 pandemic, anticipating heightened pressure on national and international mental health services soon [ 11 ]. Molebatsi et al. [ 23 ] highlighted the importance of investing in psychological support services and integrating them into national healthcare systems [ 23 ]. Therefore, policymakers are urged to prioritize mental health services and research by paying attention to policies, budgets, and the allocation of financial resources, considering the long-term impact of pandemics on mental well-being [ 41 ]. Additionally, advocating for public budget support to ensure access to mental health treatments is recommended [ 15 , 42 ]. It should be noted that not allocating enough funds to mental health programs will multiply short-term and long-term mental impacts caused by epidemics.

Another essential component of mental health response during epidemics is strengthening mental health infrastructure. While prioritizing the development of mental health infrastructure is critical, particularly in low- and middle-income countries [ 24 ], our study emphasizes the necessity of integrated healthcare systems. Such systems should effectively bridge the gap between physical and mental health, ensuring accessibility, cost-effectiveness, and seamless integration of mental health services into primary care [ 25 , 39 , 43 ]. As highlighted in previous research, there's a growing recognition of the importance of integrating mental health services into primary care settings to enhance their reach and reduce stigma [ 39 ]. It's crucial to acknowledge the establishment of a public response telephone system like the 4030 system during the COVID-19 pandemic in Iran. The Ministry of Health and Medical Education in Iran has launched a psychological assessment platform to provide additional support for mental health initiatives. Additionally, recognizing the role of non-governmental organizations (NGOs) and their contributions during emergencies in Iran is essential for understanding the comprehensive response to such crises.

The results of this study emphasized that in addition to the surveillance systems for infectious diseases, health systems need to establish mental health surveillance systems. The importance of monitoring mental health before, during, and after epidemics was underscored by participants as a key response strategy. Evaluation and monitoring of the mental/psychiatric conditions of affected populations should be part of the intervention in the early stages of a pandemic and extend beyond, incorporating programs to monitor mental health for sufficient responsiveness to anticipated mental health issues [ 10 ]. Additionally, supporting the continuity and sustainability of monitoring after the acute phase of an epidemic is crucial, as mental health issues may persist or emerge later [ 25 , 44 ]. While our study highlights the significance of this aspect, it's essential to consider practical solutions for monitoring and supervision, especially in the context of Iran. Drawing from global experiences, successful initiatives such as those observed in European countries, where electronic health records (EHR) were utilized to track mental health trends during the COVID-19 pandemic, offer valuable insights. These initiatives involved collecting data on new episodes of depression or anxiety, prescription patterns, and healthcare visits related to mental health issues [ 45 ]. Implementing similar mechanisms in Iran could provide a robust framework for monitoring and addressing mental health concerns during and after epidemics. Additionally, integrating pre-pandemic data with longitudinal follow-up assessments, as demonstrated in other studies, can offer unique insights into vulnerabilities and inform targeted interventions [ 46 ]. By leveraging international experiences and adapting successful strategies to the Iranian context, we can enhance our capacity for monitoring and addressing mental health needs throughout epidemic situations.

Research complements monitoring efforts by providing valuable insights into mental health trends and responses during epidemics. It serves as a crucial tool for understanding the effectiveness of monitoring strategies and informing targeted interventions. Participants underscored the critical need for attention to research, emphasizing the importance of increasing investment in mental health research. This investment can generate evidence to guide the development and implementation of effective mental health policies and programs [ 23 ]. Furthermore, conducting studies in various subgroups and prospective studies to assess changes over time can deepen our understanding of social and psychological responses. Additionally, examining the impact of social media and past experiences regarding disease outbreaks can provide valuable insights into developing more targeted interventions [ 47 , 48 ].

The participants emphasized the importance of the participation of various sectors and coordination among them as strategies during the response phase of epidemics. In the study by Molebatsi et al. [ 23 ], enhancing the collaboration and participation among different sectors and stakeholders involved in mental health was highlighted to ensure a comprehensive and coordinated response to the mental health needs of individuals and communities affected during the COVID-19 pandemic [ 23 ]. Beckstein et al. [ 49 ] also emphasized collaborative efforts. The results of this study highlighted the importance of collaboration among mental health professionals, healthcare providers, policymakers, and social organizations for developing comprehensive strategies and programs to address mental health needs during the outbreaks of communicable diseases [ 49 ]. In this regard, the comprehensive approach implemented in China for an effective mental health response, which is coordinated and facilitated through various systems, including government, academic societies, universities, hospitals, and non-profit organizations, is noteworthy [ 42 ].

Identification and attention to vulnerable groups in terms of mental health constitute another response strategy during epidemics. Various studies have emphasized supporting vulnerable groups [ 23 , 24 , 41 , 49 ]. Since specific populations may be disproportionately affected by the mental health impacts of epidemics, targeted interventions for vulnerable populations, including children, adolescents, older adults, individuals with pre-existing mental health conditions, and marginalized communities, are necessary [ 39 ]. Efforts to provide targeted mental health support in vulnerable populations may include ensuring access to mental health services, addressing social determinants of mental health, promoting equity in healthcare [ 43 ], identifying individuals susceptible to mental disorders, and implementing measures to maintain and improve their mental health [ 50 ].

Many studies have shown that it is not possible to improve mental health without education, inter-sectorial coordination and public participation. Based on this, investing in community-oriented measures and facilitating Community Based Organizations, and other community groups for mental health interventions should be given serious attention by health managers and policy makers. It is crucial to strengthen community-based mental health services to ensure accessibility and responsiveness to the needs of individuals and communities [ 23 , 24 , 51 ]. Mental health services should be community-based, evidence-based, accessible, fair, and proportionate to the existing mental health capacity [ 6 ]. Various studies have emphasized the importance of psychological first aid and the provision of necessary educational programs in this regard [ 25 ]. Implementing routine protocols for screening and assessing mental health, early intervention, and appropriate referral for treatment [ 39 ], raising awareness among the general population and healthcare providers about clinical manifestations of the disease for early diagnosis [ 47 ], establishing multidisciplinary mental health teams, creating safe counseling services with better access for disadvantaged individuals, and implementing mechanisms for monitoring, reporting, and intervening in suicides have also been mentioned [ 10 ]. Furthermore, the need to maintain the continuity of mental health services is emphasized, and access to psychological assistance should be available whenever needed, with sensitivity to specific arrangements related to the pandemic [ 47 ].

During epidemics, effective communication and information management regarding mental health are crucial. Providing accurate information from credible sources helps reassure the public and prevents the spread of rumors [ 50 ]. Governments and health authorities should promptly address misinformation, ensuring public security and psychological well-being [ 52 ]. Access to up-to-date information about the disease spread is vital as emphasized by Chew QH and colleagues (2020) [ 47 ]. Strategies such as limiting news consumption, avoiding misinformation, and relying on credible sources are recommended [ 9 ]. Establishing a robust health information management system is necessary for monitoring mental health care in communities [ 15 ]. Furthermore, researchers can leverage artificial intelligence for predictive models and community-based interventions, focusing on developing innovative digital solutions for information systems to enhance mental health communication during crises [ 6 ].

Public education and awareness-building about mental health are crucial during epidemics [ 22 , 24 , 25 , 41 , 53 ]. Initiatives include anti-stigma awareness programs, educational campaigns, and self-care strategies to reduce stigma and promote coping techniques [ 39 , 47 ]. Recommendations encompass promoting positive behaviors, engaging in physical activities, and seeking professional help when needed [ 2 , 54 ].

Technology and telemedicine play a vital role in mental health response, with a focus on digital interventions and remote services [ 24 , 29 , 55 ]. Teletherapy and online mental health services offer effective alternatives to in-person treatment, ensuring continuous access to support during quarantine [ 25 ]. Decision-makers should prioritize the development of digital strategies for mental health care, considering factors like social inequalities and digital divides. Telemedicine and digital psychiatry hold promise for future disaster response, but improvements are necessary [ 10 ].

Social support is a vital aspect of mental health response during epidemics [ 39 , 41 , 47 , 54 , 56 ] Community-centered programs and initiatives, including virtual support groups, online forums, and helplines, enhance social connections and reduce isolation [ 39 , 41 ]. Utilizing technology and social media facilitates communication and fosters optimism for coping with the epidemic [ 47 ]. Clear communication between authorities and the public is crucial at the policy level of healthcare [ 54 ]. Building public trust horizontally among people and vertically between the public and their institutions is recommended [ 56 ]. Additionally, emphasis is placed on building resilience through community participation and social psychological support [ 29 ]. Community resilience is essential for pandemic preparedness [ 7 ] and strengthening the healthcare system [ 50 ].

Economic support is crucial in the mental health response to epidemics, addressing socio-economic inequalities and supporting vulnerable populations [ 15 , 57 ]. McGrath et al. [ 57 ] highlighted the importance of mitigating financial hardships resulting from the COVID-19 pandemic to improve mental health outcomes. Social determinants of health, such as economic status, can be modified through community-focused interventions, including services like debt advice, food insecurity interventions, and active labor market programs [ 57 ]. Maulik et al. [ 6 ] also emphasized the need for support from civil societies and employers to cope with the increasing mental pressure [ 6 ].

Considering the qualitative nature of our study, it is essential to acknowledge several limitations. Firstly, due to challenges in accessing all individuals involved in mental health during the crisis, our sample size was small, which limits the generalizability of our findings. Additionally, conducting some interviews virtually may have impacted the depth of data collected compared to face-to-face interactions. Moreover, the participants were predominantly from psychology and psychiatry backgrounds, potentially leading to a skewed perspective and overlooking viewpoints from other stakeholders, such as the broader community. Including diverse perspectives could have enriched our study and provided more comprehensive insights. These limitations underscore the importance of replicating our findings in different settings to validate the proposed strategies and enhance mental health preparedness for future crises and community well-being.

Effective management of epidemics necessitates the implementation of tailored mental health responses. This requires a concerted effort from policymakers, managers, and decision-makers within the mental health domain to prioritize comprehensive planning, education, research, and the development of technological infrastructure. Drawing from the invaluable lessons learned during the COVID-19 pandemic, it is crucial to guide the implementation of training programs, guidelines, and resource allocation based on these experiences. The insights and experiences of managers and experts in mental health, rooted in their expertise and knowledge, have significantly influenced the conclusions of this study. These contributions enrich the healthcare system with invaluable resources, empowering us to enhance epidemic response strategies significantly.

Availability of data and materials

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

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Acknowledgements

The authors would like to thank all study participants who gave us their precious time.

This study was funded by the Tehran University of Medical Sciences. The authors appreciate the financial support of the Tehran University of Medical Sciences in IRAN.

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AOT, KHA and AZ researched the background for the project and AOT, KHA, AY, MA and MN contributed in performing study. AY, MA, MN and KHA analyzed and interpreted the data. AY, KHA, AOT and AZ edited the manuscript. All the authors read and approved the final manuscript.

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In this research, which was part of a doctoral dissertation, the study protocol received approval from the Institutional Review Board (IRB) of Tehran University of Medical Sciences, in accordance with the principles outlined in the Declaration of Helsinki. Additionally, the Ethics Committee of the same university granted approval for this research under the ID IR. TUMS.SPH.REC.1401.190. Informed consent was obtained from the participants, and confidentiality of information (including names, interview recordings, writings) was maintained, with the use of codes instead of names. The participants had the right to withdraw from the study at any time, and the possibility of sharing the results was available upon request by the participants.

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Role perceptions and experiences of adult children in remote glucose management for older parents with type 2 diabetes mellitus: a qualitative study

• Xiang Ye 1 , 2   na1 ,
• Rongzhen Liu 1   na1 ,
• Shangjie Che 2 ,
• Yanqun Zhang 3 ,
• Jiaqi Wu 1 , 2 ,
• Ya Jiang 1 ,
• Xiangrong Luo 1 &
• Cuihua Xie 1  

BMC Geriatrics volume  24 , Article number:  653 ( 2024 ) Cite this article

Metrics details

With the advent of the smart phone era, managing blood glucose at home through apps will become more common for older individuals with diabetes. Adult children play important roles in glucose management of older parents. Few studies have explored how adult children really feel about engaging in the glucose management of their older parents with type 2 diabetes mellitus (T2DM) through mobile apps. This study provides insights into the role perceptions and experiences of adult children of older parents with T2DM participating in glucose management through mobile apps.

In this qualitative study, 16 adult children of older parents with T2DM, who had used mobile apps to manage blood glucose for 6 months, were recruited through purposive sampling. Semi-structured, in-depth, face-to-face interviews to explore their role perceptions and experiences in remotely managing their older parents’ blood glucose were conducted. The Consolidated Criteria for Reporting Qualitative Research (COREQ) were followed to ensure rigor in the study. The data collected were analyzed by applying Colaizzi’s seven-step qualitative analysis method.

Six themes and eight sub-themes were identified in this study. Adult children’s perceived roles in glucose management of older parents with T2DM through mobile apps could be categorized into four themes: health decision-maker, remote supervisor, health educator and emotional supporter. The experiences of participation could be categorized into two themes: facilitators to participation and barriers to participation.

Some barriers existed for adult children of older parents with T2DM participating in glucose management through mobile apps; however, the findings of this study were generally positive. It was beneficial and feasible for adult children to co-manage the blood glucose of older parents. Co-managing blood glucose levels in older parents with T2DM can enhance both adherence rates and confidence in managing blood glucose effectively.

Peer Review reports

According to the latest data from the International Diabetes Federation, the global prevalence of T2DM in adults was 537 million people in 2021, and a projected 783 million people will be living with diabetes by 2045 [ 1 ]. Surveys from 2015 to 2017 showed that the prevalence of diabetes in China was above 20% in older adults aged 60 years and over [ 2 ]. According to data from China’s National Bureau of Statistics, 190 million people aged 65 years and over were living with diabetes in China by the end of 2020, accounting for 13.5% of the total population [ 3 ]. Population aging has become a major issue, challenging the sustainability of healthcare and social care services.

Mobile apps are effective tools for managing blood glucose, diet, activity and wounds [ 4 , 5 , 6 , 7 ]. Older adults as a target group may benefit from using apps [ 8 ]. Nevertheless, older adults face many barriers in using technology for healthcare decision-making, including issues with familiarity, willingness to ask for help, trust of the technology, privacy and design challenges [ 9 ]. Older adults may need support and assistance from their family members when they use mobile apps, and family support is mostly from their spouses and children [ 10 ]. Studies are lacking on how adult children really feel about engaging in the glucose management of their older parents with T2DM through mobile apps. Therefore, this study aimed to explore the role perceptions and experiences of adult children involved in remote glucose management of older parents with T2DM.

Study design

This qualitative study used a phenomenological approach involving a series of personal, semi-structured interviews. This study was conducted based on the COREQ [ 11 ]. It was conducted from May 2022 to February 2023 at the Department of Endocrinology and Metabolism of a tertiary care hospital in Guangzhou, China.

Recruitment and participants

To capture diverse insights relevant to our research theme, purposive sampling was employed. The inclusion criteria were as follows: ① participants with a father/mother aged ≥ 65 years diagnosed with type 2 diabetes [ 12 ]; ② participants aged ≥ 18 years and <65 years; ③ followed the app of Sinomedisite Glucose Manager via WeChat and checked father’s/mother’s blood glucose data at least once a week for ≥ 6 months; ④ no reading and communication disorders. Exclusion criteria included individuals with cognitive impairment, psychiatric disorders (such as depression, anxiety disorders, schizophrenia, and bipolar disorders), hearing impairments, or those who were unable to independently communicate verbally in Mandarin. Sample size was based on data saturation. Each participant was provided with a glucometer and complimentary access to the management app. Ethical approval for this study was obtained from the ethical committees of Nanfang Hospital of Southern Medical University (NFSC-2022-002) and written informed consent was provided by all participants.

App description

The app of Sinomedisite Glucose Manager has the following features:

Automatic uploading of blood glucose data: The blood glucose data can be automatically uploaded through the wireless network.

Data recording: Parents can record their diet, exercise, etc. in the APP.

Data analysis: The APP can intelligently generate blood glucose curves, the average fasting blood glucose, the average postprandial blood glucose, the adherence rate for blood glucose control, the incidence of hyperglycemia and hypoglycemia.

Knowledge and video section: The APP is e-Health literacy friendly and can provide information on diabetes through text or video formats.

Communication: Parents can send messages to administrator for assistance through the APP.

Co-managing blood glucose: Adult children can receive real-time updates of their parents’ blood glucose levels and reminder of abnormal readings through the APP.

Focus group guide

The final focus group guide (Table  1 ) used in this study was developed based on the COREQ, literature, discussions within the research team, expert consultations and pre-interviews. To minimize bias, a trained researcher conducted the semi-structured interviews. At the conclusion of each session, interviewees were prompted to add any further comments.

Data analysis

The data were analyzed using Colaizzi’s seven-step qualitative analysis method. The transcription and analysis processes were conducted concurrently by 2 trained reviewers. The audio recordings were transcribed into text within 24 h following the interviews, and any ambiguities or uncertainties were clarified or verified by consulting the interviewees. Nvivo12 supported the organization of codes and themes during data analysis. The textual data transcribed from each interview were imported into the Nvivo12 project. After meticulously reviewing the original materials, two independent reviewers extracted key phrases and sentences, coding them individually. Discrepancies were discussed and resolved until a 90% consensus was achieved. Persistent disagreements were adjudicated by an expert in qualitative research, based on the inputs from the two reviewers. The final nodes and themes were agreed upon by the research team to reduce bias and verified by re-engaging with the interviewees. After data analysis, representative quotes for each theme and sub-theme are selected and presented in Supplementary file 1 .

Demographic data

After interviewing 14 patients, no new themes emerged in the last two interviews, indicating that data saturation had been reached. Hence, we stopped the interviews. Sixteen face-to-face, semi-structured interviews were conducted in a separate examination room in the department, each lasting approximately 15–40 min, which were audio-recorded with participants consent. Demographic characteristics of interviewees are shown in Table  2 .

Among adult children’s role perceptions, the following four themes were identified: (1) health decision-maker; (2) remote supervisor; (3) health educator; and (4) emotional supporter. Among adult children’s experiences of participation, the following two themes were identified: (1) Facilitators to participation; and (2) Barriers to participation. The details of these themes are shown in Table  3 .

Role perceptions of adult children co-managing blood glucose for older parents with T2DM using a mobile app

1. Health decision-maker Older parents with T2DM often experience a higher prevalence of cognitive dysfunction, which may impair their ability to make timely and appropriate health decisions. Adult children can keep track of their parents’ blood glucose through the mobile app. Information sharing can help adult children provide timely and appropriate decision-making for older parents with T2DM.

N7: “As soon as I received the message that his blood sugar was 3.2 mmol/L , I called him to drink sugar water and test his blood sugar again 15 minutes later. I reduced his bedtime insulin by 4 units after consulting with the doctor. After that , he rarely suffered from hypoglycemia.”

N15: “The mobile app revealed that the adherence rate for blood sugar control was only 50%. The reason for blood sugar levels not meeting the standard may be related to excessive rice consumption. Therefore , I suggested she measure postprandial blood sugar , which was 16 mmol/L. Following the dietitian’s advice , I recommended she limit her rice intake to 75 g per meal. As she was unsure how to measure the rice , I bought an electronic scale and taught her how to use it to control her staple food intake. Consequently , her adherence rate improved from 50–60%.”

2. Remote supervisor Some interviewees said that they were far away from their parents. By viewing their parents’ blood glucose data through the mobile app, they could track blood glucose data and remind their parents to address abnormal blood glucose levels by taking medication and adjusting their diet when experiencing irregular readings.

N7: “Although my father doesn’t live with me , I can keep an eye on his blood sugar through the mobile app. When his blood sugar is lower than 5.6 mmol/L , I remind him to eat 3 pieces of soda crackers or drink a glass of milk.”

N9: “As my father is old and has a bad memory , he would miss his medication. I checked to see if his blood sugar was up to standard via the mobile app and reminded him daily not to miss his medication.”

3. Health educators Adult children can give timely guidance to their parents when they see abnormal blood glucose data via the mobile app, breaking the constraints of time and space, and helping their parents adopt behaviors conducive to optimizing blood glucose.

N2: “I can check his blood sugar data anytime and anywhere through the app. One day he ate plain congee resulting in high blood sugar. Then I told him not to drink plain congee. If he wants it , he should add beans , wheat , and lean meat to plain congee.”

N4: “ I received a message which showed that she suffered from hypoglycemia. It turned out that exercising on an empty stomach caused her hypoglycemia. I stressed the precautions to be taken in exercising. After that , she never exercises on an empty stomach.”

4. Emotional supporter older individuals with diabetes are prone to psychological problems such as depression, anxiety and fear because of the long duration of the disease, complications, hypoglycemia and other unexpected situations. Adult children can provide emotional support to parents in time, alleviating their negative emotions and increasing their confidence in glucose control.

N1: “I saw his blood sugar fluctuating wildly via the mobile app and called him to ask why. He said he had poor sleep and wondered if he was suffering from depression. I comforted him that everything would be fine. After two weeks of adjustments , his mood was improved.”

N6: “My mother had anxiety and was terrified of hypoglycemia. I told her to take it easy and monitor her blood sugar regularly so that I could keep track of her blood sugar. I taught her ways to cope with hypoglycemia. She’s not so scared anymore and confident in her blood sugar control.”

The experiences of participation in the glucose management for older parents with T2DM using a mobile app

1. Facilitators to participation There are facilitators to participation, such as convenience, app user-friendly, digital empowerment and family-based health promotion.

1.1 Convenience Adult children can access blood glucose data online, eliminating the need to look through manual records.

N7: “Although my father doesn’t live with me , I can keep an eye on his blood sugar anytime and anywhere through the mobile app.”

N8: “I used to have to keep track of his blood sugar through his paper records. But now I can check his blood sugar through the app. It’s very convenient.”

1.2 App user-friendly The blood glucose data are presented in a visual curve, which makes management more intuitive and accurate. The app can display the adherence rate for blood glucose control and the incidence of hyperglycemia and hypoglycemia in a variety of graphical outputs.

N1:“The blood sugar data are presented in a visual curve , which makes management more intuitive and accurate. So I find it very , very useful.”

N12: “Information collected can be presented in a variety of graphical outputs. It’s so easy and intuitive now that I no longer worry her about misremembering or missing records.”

1.3 Digital empowerment Digital blood glucose management enables adult children to use the mobile app to improve the ability and efficiency of their older parents with T2DM to manage blood glucose levels, instead of relying solely on healthcare professionals.

N6: “From the time I told her blood sugar control goals and taught her how to check her adherence rate via the mobile app , she paid more attention to her blood sugar and monitored blood sugar regularly.”

N14: “I learned that exercising after meal can lower postprandial blood sugar through the app. Based on her exercise and blood sugar , I worked with her on an exercise programme which increased her adherence rate for blood sugar control to 70%.”

1.4 Family-based health promotion Engagement of adult children in managing the blood glucose levels of their older parents with T2DM enhances familial support. This includes aiding in dietary choices and financing treatment, thereby improving glucose control and promoting health.

N8: “He has diabetic nephropathy. In order to control his protein intake and blood sugar , I help him prepare breakfast , lunch and dinner. The mobile app showed that his adherence rate has increased from 50–80%.”

N13: “I and my sister kept track of her blood sugar via the app and bought her medication monthly. After a period of medication , her blood sugar was under control.”

2. Barriers to participation There are barriers to participation, such as privacy and security concerns, adaptation and learning, lack of knowledge about glucose management, and concerns oven the cost of test strips.

2.1 Privacy and security concerns A few interviewees stated that they had concerns about entering personal information into a mobile app owing to the fear of security breaches.

N5: “While we can help the elderly manage their blood sugar through mobile apps , I am worried that my mother’s information will be leaked.”

N7: “Nowadays there are a lot of scammers. The elderly are easily deceived. I am afraid that the personal information which is entered into the app will be utilized by an illegal actor after it is leaked.”

2.2 Adaptation and learning It takes time to learn and get used to the new application.

N8: “I wasn’t used to use the app at first.”

N11: “After 2 weeks of adaptation and consulting with doctors and nurses , I am now proficient in using the mobile app.”

2.3 Lack of knowledge about glucose management Lack of knowledge may affect adult children to take part in glucose management of older parents.

N3:“I didn’t know his blood sugar control goals. There’s no point in focusing on his blood sugar via app.”

N11: “One day his blood sugar was 3.8 mmol/L. I was afraid that he would have hypoglycemic symptoms and told him not to take his pre-breakfast insulin injection. As a result , his blood sugar was 18 mmol/L in the 2 h after the meal. When an emergency occurs , I still don’t know what to do.”

2.4 Concerns oven the cost of test strips Several adult children expressed concerns regarding the high cost of test strips, which were perceived as unaffordable. This financial burden could deter both older parents and their children from purchasing these essential supplies, resulting in less frequent blood glucose monitoring by the older parents. Consequently, this infrequent monitoring could adversely affect the reliability of the glucose data accessed via apps by adult children, diminishing their motivation and efficacy in jointly managing their older parents’ condition.

N4: “The test strips are too expensive. I have already bought her test strips twice , which were not reimbursable.”

N9:“Do you provide complimentary test strips? He hesitates to incur expenses , thus rarely monitors his blood sugar. Consequently , I can access only limited data regarding his blood sugar levels.”

N16: “The test strips are so expensive that I can’t afford them.”

The current qualitative study provided insight into the role perceptions and experiences of adult children of older parents with T2DM in participating in glucose management through a mobile app. Six themes and eight sub-themes were identified in this study.

Family-involvement intervention is helpful in diabetes management [ 13 ]. Higher levels of social support are often associated with increased disease knowledge, better medication adherence, better self-efficacy and glucose control [ 13 , 14 , 15 , 16 , 17 ]. As important family members of older parents with T2DM, adult children assume an important role in geriatric diabetes care. The results of this study showed that most of adult children were able to provide health decision-making, remote supervision, health education, and emotional support to older parents with T2DM by participating in glucose management through a mobile app.

Significant and positive associations have been found between diabetes and anxiety disorders [ 18 ]. Diabetes and depression are frequently comorbid in older adults [ 19 ]. Older adults with diabetes have unique psychological and medical challenges that impact self-care and glucose control [ 20 ]. Most adult children in this study were able to provide emotional and psychological support for their older parents, which alleviate their negative emotions and increase their confidence in glucose control.

Mobile apps can improve self-management behavior of patients in terms of dietary control, physical exercise, blood glucose monitoring, medication adherence, and screening of complications [ 17 , 21 , 22 , 23 ]. Studies have shown that mobile apps help individuals with diabetes to control their blood glucose effectively [ 24 , 25 , 26 , 27 , 28 , 29 ]. However, older adults face many barriers in using technology for healthcare decision-making, including issues with familiarity, privacy, and design challenges [ 9 ]. Therefore, there is a need for adult children to use these mobile apps to assist their older parents in glucose management.

Many studies have reported that issues of privacy and security are a major concern [ 9 , 30 ], and our study echoed this. These problems are further exacerbated when traditional paper records are transferred to an electronic medium. We have found many important challenges in implementing a secure healthcare monitoring system using medical sensors [ 31 ]. A systematic review and meta-analysis indicated that older adults are susceptible to fraud [ 32 ]. In this study, some interviewees expressed the concerns about the privacy and security of the mobile app, fearing potential information leak about their parents. In order to protect the security and privacy of older adults, we have enhanced testing and maintenance for the APP and assured that the information will be kept confidential.

Technical barriers can result in decreasing intention to use the app [ 33 ]. This study showed that several adult children had difficulties in applying mobile apps and needed time to adapt. Healthcare professionals should instruct adult children of older parents in the use of mobile apps and appraise them if necessary to make sure that they have mastered use of the apps. Barriers to app use include participant’s technological literacy and lack of knowledge and awareness of apps as healthcare tools [ 34 ]. So we need to improve the knowledge and awareness of apps as effective tools to control blood glucose. Several interviewees in this study reported that app motivated them to learn about diabetes and improved the ability and efficiency of the older parents to control their blood glucose instead of relying solely on healthcare providers.

This study showed that a small proportion of adult children lacked knowledge about glucose management. Family members involved in a patient’s diabetes management may impede the patient’s self-care and compromise glucose control unless the family members are taught to avoid obstructive behaviors [ 35 ]. Non-supportive behaviors include nagging, arguing, getting in the way of patient’s self-care, food temptation, visible irritation, and refusing to share the burden of living with diabetes [ 36 , 37 , 38 ]. Non-supportive behaviors among family members are thought to be associated with patients being less adherent to their diabetes medication regimen, and being less adherent is associated with worse glucose control [ 39 ]. Family members with knowledge of diabetes can provide supportive behaviors and participate in glucose management of older adults with diabetes. Studies have shown that people with diabetes have significantly higher levels of health literacy and diabetes knowledge when they care for themselves with additional caretaker assistance [ 14 ]. Health knowledge can have an impact on the self-management abilities by bolstering individuals’ confidence to modify health behaviors, and has positive implications for health outcomes among older adults residing in social housing [ 40 ]. The knowledge of patients and their key supporters should be regularly assessed in future practice. In the development and implementation of telehealth programs, the level of health literacy of caretakers should be fully considered. Furthermore, training programs should be developed for key supporters to improve their knowledge of diabetes.

Adult children’s attention to the blood glucose levels of their parents through mobile apps relies on the patient performing blood glucose monitoring. If older parents with diabetes do not monitor their blood glucose, adult children will not be able to participate in glucose management. Some interviewees in this study indicated that the test strips were so expensive that they could not afford them. This is consistent with a previous study, in which the cost of self-monitoring of blood glucose (SMBG) was the main reason why participants did not practice SMBG regularly [ 41 ]. This financial burden could deter both older parents and their children from purchasing these essential supplies, resulting in less frequent blood glucose monitoring by the older parents. The state could reimburse a percentage of the cost of test strips for older individuals with diabetes when developing health insurance policies. Healthcare professionals should set a personalized frequency of SMBG for older people with diabetes.

Strengths and limitations

The current study had several noteworthy strengths. First, the current study reported according to the COREQ. Second, in order to increase the reliability of the study, any differences in design, methodology, data analysis and results were discussed by the research team until agreement was reached. Third, all data were analyzed by the study team. Interviewees were audio-recorded to ensure the authenticity of the findings and, during the course of the study, the researchers retained the focus group guide, recorded data, original transcriptions, and study results.

Nevertheless, there were limitations to the current study. Interviewees were recruited from a tertiary care hospital. Thus, the sample was not representative of all adult children’s perceptions of co-managing the blood glucose of their older parents with T2DM through a mobile app. Therefore, the findings and conclusions should be interpreted with caution in terms of universality. Future research is needed to (1) evaluate the usability of mobile app in older adults with diabetes and (2) assess the effectiveness of adult children’s involvement in glucose management of older parents with diabetes via mobile app through randomized controlled trials, trials with larger sample sizes, trials with effective recruitment strategies and sampling methods, and trials in different settings such as primary care.

Although there were barriers to participation in glucose management of older parents with T2DM through a mobile app, the findings of this study were generally positive. It was beneficial and feasible for adult children to co-manage the blood glucose of their older parents. Co-managing blood glucose levels in older parents with T2DM can enhance both adherence rates and confidence in managing blood glucose effectively.

The results of the current study can provide the basis for the development of telemedicine and telehealth in geriatric care, and promote the in-depth application of internet, mobile apps, and other information technologies in elderly services.

Data availability

The data generated and/or analyzed during the study are not publicly available but are available from the authors upon reasonable request and with permission of the corresponding author.

Abbreviations

Type 2 Diabetes Mellitus

Consolidated Criteria for Reporting Qualitative Research

Self-Monitoring of Blood Glucose

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Acknowledgements

The authors thank all the interviewees who participated in this study.

This study and the article processing charges were funded by nursing research project of Southern Medical University (Z2021004).

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Xiang Ye and Rongzhen Liu contributed equally to this work.

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Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, No.1838, Guangzhou Avenue North, Baiyun District, Guangzhou, China

Xiang Ye, Rongzhen Liu, Jiaqi Wu, Ya Jiang, Xiangrong Luo & Cuihua Xie

School of Nursing, Southern Medical University, Guangzhou, China

Xiang Ye, Shangjie Che & Jiaqi Wu

Department of Emergency, Nanfang Hospital, Southern Medical University, No.1838, Guangzhou Avenue North, Baiyun District, Guangzhou, China

Yanqun Zhang

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X.Y. and C.H.X. co-designed the study. Y.Q.Z. conducted the interviews. R.Z.L., J.Q.W., Y.J. and X.R.L. contributed to the analysis of the findings. X.Y. and S.J.C. drafted and wrote the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Cuihua Xie .

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Ye, X., Liu, R., Che, S. et al. Role perceptions and experiences of adult children in remote glucose management for older parents with type 2 diabetes mellitus: a qualitative study. BMC Geriatr 24 , 653 (2024). https://doi.org/10.1186/s12877-024-05224-6

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• Adult children
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• Older parents
• Type 2 diabetes mellitus
• Qualitative study

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“I didn’t even wonder why I was on the floor” – mixed methods exploration of stroke awareness and help-seeking behaviour at stroke symptom onset

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 , 2 ,
• Christina Stang 1 ,
• Franziska Herzog   ORCID: orcid.org/0000-0002-2504-294X 3 ,
• Melek Sert 1 ,
• Johanna Hoffmann 1 ,
• Jan Purrucker   ORCID: orcid.org/0000-0003-2978-4972 1 ,
• Fatih Seker   ORCID: orcid.org/0000-0001-6072-0438 4 ,
• Martin Bendszus   ORCID: orcid.org/0000-0002-9094-6769 4 ,
• Wolfgang Wick   ORCID: orcid.org/0000-0002-6171-634X 1 ,
• Matthias Ungerer 1   na1 &
• Christoph Gumbinger   ORCID: orcid.org/0000-0002-6137-1169 1   na1  

BMC Health Services Research volume  24 , Article number:  880 ( 2024 ) Cite this article

Metrics details

Introduction

To better target stroke awareness efforts (pre and post first stroke) and thereby decrease the time window for help-seeking, this study aims to assess quantitatively whether stroke awareness is associated with appropriate help-seeking at symptom onset, and to investigate qualitatively why this may (not) be the case.

This study conducted in a German regional stroke network comprises a convergent quantitative-dominant, hypothesis-driven mixed methods design including 462 quantitative patient questionnaires combined with qualitative interviews with 28 patients and seven relatives. Quantitative associations were identified using Pearson’s correlation analysis. Open coding was performed on interview transcripts before the quantitative results were used to further focus qualitative analysis. Joint display analysis was conducted to mix data strands. Cooperation with the Patient Council of the Department of Neurology ensured patient involvement in the study.

Our hypothesis that stroke awareness would be associated with appropriate help-seeking behaviour at stroke symptom onset was partially supported by the quantitative data, i.e. showing associations between some dimensions of stroke awareness and appropriate help-seeking, but not others. For example, knowing stroke symptoms is correlated with recognising one’s own symptoms as stroke ( r  = 0.101; p  = 0.030*; N  = 459) but not with no hesitation before calling help ( r  = 0.003; p  = 0.941; N  = 457). A previous stroke also makes it more likely to recognise one’s own symptoms as stroke ( r  = 0.114; p  = 0.015*; N  = 459), but not to be transported by emergency ambulance ( r  = 0.08; p  = 0.872; N  = 462) or to arrive at the hospital on time ( r  = 0.02; p  = 0.677; N  = 459). Qualitative results showed concordance, discordance or provided potential explanations for quantitative findings. For example, qualitative data showed processes of denial on the part of patients and the important role of relatives in initiating appropriate help-seeking behaviour on patients’ behalf.

Conclusions

Our study provides insights into the complexities of the decision-making process at stroke symptom onset. As our findings suggest processes of denial and inabilities to translate abstract disease knowledge into correct actions, we recommend to address relatives as potential saviours of loved ones, increased use of specific situational examples (e.g. lying on the bathroom floor) and the involvement of patient representatives in the preparation of informational resources and campaigns. Future research should include mixed methods research from one sample and more attention to potential reporting inconsistencies.

Peer Review reports

Acute ischemic stroke is one of the leading causes of death and acquired disability worldwide. Acute treatment options include stroke unit treatment, intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT), all with strongly time-dependent treatment effects. While institutional and regulatory efforts have addressed the time frames from emergency call to treatment initiation [ 1 , 2 , 3 , 4 , 5 ], the time from symptom onset to first help-seeking is largely determined by decisions made by individual medical laypeople. Efforts for raising awareness of stroke are usually based on the assumption that increased stroke awareness will contribute to an increased likelihood of patients behaving correctly, and thereby an increased likelihood of timely treatment access.

However, a positive effect of these efforts has not been shown consistently [ 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Moreover, evaluations use a wide range of outcome measures, including knowledge of risk factors, symptoms and treatments [ 9 , 10 , 11 , 12 , 13 , 14 ], action taken [ 9 ], emergency department visits [ 8 ], thrombolysis rates [ 8 ], initiation of reperfusion therapy [ 15 ] or functional outcome at discharge [ 7 ] – all capturing different aspects of how well a person is informed about stroke, knows what to do or actually implements the recommended action. This means that it is not clear to what extent knowledge of stroke symptoms can actually predict good health outcomes, or whether timely presentation to emergency services can really be attributed to higher stroke awareness. Several qualitative studies have pointed out the complexity of the decision-making-process, which in addition to patient-specific factors, is also subject to outside influences [ 16 , 17 , 18 ].

This study aims to (1) assess quantitatively whether different aspects of stroke awareness were associated with appropriate help-seeking behaviour at stroke symptom onset, and to (2) investigate qualitatively why this may (not) have been the case. We expect our results to help inform outreach campaigns and awareness efforts to better reach its target groups and intended goals for improved stroke outcomes.

Mixed methods research design

This study used a convergent quantitative-dominant, hypothesis-driven mixed methods design including patient questionnaires and semi-structured interviews with patients and relatives (Fig.  1 ). The theoretical framework is informed by the COMIC Model, developed for the evaluation of complex care interventions, such as stroke care provision. It focuses on aspects beyond the medical (such as patient-centeredness) and specifically considers the context in which an intervention is implemented, as needed for the current study [ 19 ]. The study was conducted in a German regional stroke network (FAST; www.fast-schlaganfall.de ). Ethics approval was obtained from the Medical Faculty of Heidelberg University (S-306/2016; S-682/2017). All study participants provided written informed consent. We report our findings in line with applicable standards [ 20 ].

The mixed methods integration strategy was to compare (especially regarding patient data) and to expand (especially regarding relative data) [ 21 ]. The mixed methods research data inventory [ 21 ] is shown in Table  1 . We hypothesised that stroke awareness would be associated with appropriate help-seeking behaviour at stroke symptom onset. We defined “stroke awareness” as having information about stroke before the stroke occurred using the concepts “knowing stroke symptoms”, “familiarity with information campaigns”, “having experienced one or more previous strokes” and “knowing other stroke patients” or “having discussed stroke symptoms with other stroke patients”. We defined appropriate help-seeking as responding to a suspected stroke by seeking the appropriate help immediately upon symptom onset, measured using the concepts “recognising the symptoms as stroke”, “no hesitation before calling for help”, “transportation to hospital by emergency ambulance”, and “arrival at hospital within the 4.5 h therapeutic time window”. For mixing of data strands, we conducted a joint display analysis to assess for “fit” and draw meta-inferences according to the categories of concordance, expansion, complementarity or discordance of quantitative and qualitative findings which are addressed in the Discussion [ 22 , 23 ].

Data collection and analysis

Quantitative and qualitative data were collected separately. The quantitative data collection consisted of a questionnaire for patients admitted with acute stroke at an urban university hospital or a rural primary stroke center. Patients were recruited consecutively over a period of 6 months, starting in January 2017. The questionnaires were completed on the day after admission by the patients and their treating physician. Quantitative data were analysed using standard descriptive statistics. Associations were identified using Pearson’s correlation analysis. More detailed information on the quantitative questionnaire is published elsewhere [ 26 ].

For the qualitative data collection, semi-structured interviews were conducted with stroke patients and their relatives. A purposive sampling strategy was used to include interviewees with different stroke pathway experiences such as different transfer modes (helicopter or ambulance), admission at more or less specialised hospitals as well as different health outcomes. Recruitment and data collection place from May to July 2018 at Heidelberg University hospital and from July to September 2019 at two primary stroke centers. Interviews were conducted in German, approximately one month after stroke. The interview guide was piloted in advance with members of a regional stroke self-help group. For qualitative intra-method analysis, interview transcripts were coded by at least two researchers using MaxQDA-software (2018, VERBI, Berlin, Germany). After coding of all transcripts was completed, the quantitative results were used to focus the qualitative analysis on the aspects of stroke awareness and help-seeking behaviour as outlined for the questionnaires.

More detailed information on the respective methods of data collection and intra-method data analysis are shown in Table  2 .

Patient and public involvement

A stroke self-help group consulted on the qualitative design and helped pilot the interviews. Stakeholder validation of preliminary results was conducted with the Patient Council of the Department of Neurology on 17 November 2020, which showed agreement with findings outside the study sample and provided insights into discordance between quantitative and qualitative findings (see Discussion).

Baseline characteristics (questionnaires)

In total, 462 patients were included in the quantitative analysis. Median age was 71.5 years (IQR: 60–79) and 47.4% of patients were female. Median premorbid Rankin scale (pmRS) was 0 (0–2). Other baseline characeristics including primary admission hospital, health status and risk factors are reported in Table  3 .

Summary of main findings

Patient and relative characteristics (interviews)

We conducted 35 interviews, including 28 patient interviews and seven relative interviews. In 8 of the patient interviews, a relative was also present and occasionally participated. The interviews lasted between 20 and 82 min (median: 47 min, IQR: 32–59). Eleven patients were female (39%), and median age was 66 years (IQR: 60–78). Most patients had no prestroke disabilities as indicated by a pmRS of 0 (IQR 0–1). The mean NIHSS at admission was 8.7 (SD 7.7), indicating that most patients had not experienced a severe stroke. The primary admission hospital of eleven patients was an EVT-capable hospital; whereas the others were admitted at an IVT-capable hospital. Mean NIHSS at discharge was 2.6 (SD 2.6) while median mRS at discharge was 2 (IQR 1–3), showing a relatively good outcome after stroke. Of the seven relatives, six were female, and median age was 58, ranging from 23 to 72 years.

Help-seeking and stroke awareness

Main findings are summarised in an integrated visual display in Fig.  2 . This includes statistical results as well as qualitative interview quotes.

Knowing stroke symptoms

Questionnaires showed a positive correlation between knowing stroke symptoms and recognising symptoms as stroke ( N  = 459; r  = 0.101; p  = 0.030*) and arrival at hospital within 4.5 h ( N  = 459; r  = 0.093; p  = 0.046*), but not with no hesitation before calling for help ( N  = 457; r  = 0.003; p  = 0,941) and transportation by emergency ambulance ( N  = 462; r  = 0.014; p  = 0.764).

Five patient interviewees reported immediately knowing or strongly suspecting that they experienced a stroke. One recognized the stroke when he felt a sudden, strong stab of pain in the head and could not hold a water bottle. The other patient recognised the stroke when she saw her drooping cheek in the mirror. Of the five patients who recognized their stroke, four patients immediately called an ambulance or told their spouse to do so. The fifth patient was alone at home and could not physically react appropriately.

In contrast, eight patients who consciously experienced their symptoms stated that they had no idea it was a stroke, e.g. specifying that “[it] was the last thing [he] would have thought of” (Patient, Interview 12). These patients reported slurred speech, not being able to speak or answer questions, not being able to sit/stand/get up or walk (properly), not being able to use their leg(s), lying on the floor, and not being able to use their arm or hand (including dropping things). Another patient specified that even though she was aware of common stroke symptoms, she did not recognise them in her own case.

I know this thing , that you hold up both arms. But for myself , it would never have crossed my mind . Patient , Interview 1

She and another patient emphasised that even though they consciously experienced one or more symptoms, they did not feel that something was wrong.

I thought I had got up to go to the bathroom. I didn’t even wonder why I was on the floor. […] I just felt so comfortably sleepy and thought: Hm , why can’t I get up? Patient , Interview 1

Sometimes patients also initially attributed their symptoms to alternative explanations, i.e. an epileptic attack or hangover. Eight patients were unconscious or too confused to notice their symptoms or did not remember the situation. In these cases, other people called for help on their behalf. Twelve relatives present at symptom onset immediately knew or strongly suspected a stroke based on the symptoms, which included slurred speech, drooping mouth, not being able to speak, paresis, not being able to get up or walk properly, a cramped-up hand, and tingling feelings in one arm.

All relatives suspecting a stroke immediately called for help without waiting for the symptoms to improve or otherwise delaying the process.

I saw that something was wrong with [her] mouth and that’s when I knew it was a stroke . Relative , Interview 6 .

Familiarity with stroke information campaigns

Questionnaires showed a positive correlation between familiarity with stroke information campaigns and recognising symptoms as stroke ( r  = 0.203; p  ≤ 0.001*; N  = 457) but no correlation with no hesitation before calling for help ( r  = 0.009; p  = 0.847; N  = 456), transportation by emergency ambulance ( r  = 0.046; p  = 0.323; N  = 460), and arrival at hospital within 4.5 h ( r  = 0.014; p  = 0.769; N  = 457).

In the interviews, patients were asked about their prior knowledge about the disease stroke and if so, their information sources. Twelve patients indicated that they had had prior information about the disease stroke, naming information sources such as television shows, books and magazines on health topics, knowing other stroke patients, medical conditions because of which they had been told they were at risk for stroke, a previous (own) stroke, and working or volunteering in health care. Of these patients, two patients reported having recognised their stroke, both immediately asking their husbands to call help. Stroke information campaigns were not mentioned by the interviewees.

Many patients who answered “no” to the question “Did you have any prior information about the disease stroke?”, also reported knowing other stroke patients or having discussed their stroke risk or suspected stroke symptoms with a health professional in the months or years before their stroke. Two patients reported actively avoiding information on the topic

When I saw those news articles , I did not read them. […] I skipped them. […] I did not want to know about that. […] I had the feeling […] that I wanted nothing to do with it. Patient , Interview 9 When there was information on TV , I often switched channels. I can’t watch it […] , it upsets me too much. Patient , Interview 34

The latter patient is one of two patients who, despite indicating no prior information about stroke, recognized their stroke at symptom onset. The other patient reported that because of his regular check-up appointments for heart disease he was aware of his stroke risk. The patient was alone at home when the stroke happened but was found by a neighbour who immediately called an ambulance.

Only few patients who indicated having no prior information about stroke also reported not knowing any stroke patients and not having been aware that they were at risk of stroke. In these cases, it was the patient’s partner who initiated help-seeking. In one case, the patient’s wife called an ambulance because of the severity of the symptoms even though she did not realise it was a stroke at the time.

Nine relatives present at symptom onset said they had prior information about stroke, also citing television shows and books on health topics, knowing other stroke patients, the patient’s previous stroke, and volunteering in health care as their main information sources .

Speaking to patient: I saved you. Because I know […]. I do read a lot , and I watch [shows] on TV Relative , Interview 33

All of these relatives recognised the patient’s stroke based on their symptoms and sought help immediately.

Previous stroke

Questionnaire data for having experienced one or more previous strokes showed a positive correlation with recognising symptoms as stroke ( r  = 0.114; p  = 0.015*; N  = 459) but no correlation with no hesitation before calling for help ( r  = 0.027; p  = 0.565; N  = 457), transportation by emergency ambulance ( r  = 0.008; p  = 0.872; N  = 462), and arrival at hospital within 4.5 h ( r  = 0.02; p  = 0.677; N  = 459).

In the qualitative patient sample, four patients had previously experienced a stroke. None of them recognised their second stroke, with two unconscious at symptom onset or unable to recall the situation later. In two cases, patients knew that a stroke had been discovered previously during a routine scan, but they had not been aware of it when it happened (so-called “silent infarctions”). A third patient had experienced his first stroke just a few weeks prior to his second while he was still in rehabilitation for the first. A fourth patient had experienced an acute stroke two years previously. This latter patient did not seem to (want to) realise that this would put him at risk for another stroke:

Interviewer: “Were you aware that having had a previous stroke would put you at risk for another one?” Interviewee: I thought it’s enough now. I […] suppressed it , [put it] out of my mind […]. I thought it would be over now. Patient , Interview 9

In one of the above cases, Patient 9’s wife recognized the stroke and alerted emergency services immediately. In the other cases, no relatives were present and emergency services were instead alerted by unrelated witnesses. A fifth case of a previous stroke was reported by the daughter of a stroke patient who was herself not included in this study. This patient had experienced a severe acute stroke approximately twelve years previously. The daughter reported this as the reason why she recognized her mother’s second stroke and called for help immediately:

She had major speech problems after her first stroke […]. And [this time] I noticed the exact same thing. […] I said: it’s a stroke again. Relative , Interview 24

Knowing other stroke patients

Questionnaires showed no correlation between knowing other stroke patients and recognising symptoms as stroke ( r  = 0.082; p  = 0.081; N  = 455), no hesitation before calling for help ( r  = 0.031; p  = 0.514; N  = 453), transportation by emergency ambulance ( r  = 0.052; p  = 0.264; N  = 458), and arrival at hospital within 4.5 h ( r  = 0.052; p  = 0.272; N  = 455). For those patients who did know other stroke patients and who reported having discussed stroke symptoms with them, a positive correlation was found with recognising symptoms as stroke ( r  = 0.152; p  = 0.026*; N  = 215), and arrival at hospital within 4.5 h ( r  = 0.230; p  = 0.001*; N  = 217) but not with no hesitation before calling for help ( r  = 0.045; p  = 0.506; N  = 216) and transportation by emergency ambulance ( r  = 0.037; p  = 0.588; N  = 217).

In the interviews, thirteen patients reported knowing other stroke patients before, mostly family members and friends, but also colleagues, neighbours and acquaintances. Of these, two patients had recognised their own stroke and called for help immediately. One spoke in detail about her son-in-law’s stroke and thrombectomy treatment as well as the stroke experience of a friend, stating this as the reason “[…] why [she and her husband] had known about stroke since then and also knew about the time window” (Patient , Interview 7) . This was not the case for the other patient who first reported no prior information about stroke before mentioning that his mother had had one at a much older age:

Interviewer: Did you have general prior information about the disease stroke? Interviewee: No. […] Well , [my] mother had a stroke at [88]. Of course , I was aware of that. But , well , riding your motorcycle at [57] , you don’t think about a stroke Patient , Interview 25

A similar pattern was also visibile with other interviewees, who initially responded that they did not know other stroke patients before realising that this was not the case. Nine patients specifically stated that they did not know other stroke patients before their own stroke. Of these, three patients were able to recognise their own stroke, however citing other information sources such as check-ups for heart disease, working in health care, and TV programs.

Seven relatives present at symptom onset reported knowing other stroke patients, with several identifying this as the reason why they recognised their spouse’s stroke and responded appropriately.

We reacted immediately […] because several people in our family already had a stroke , so I know the symptoms. Relative , Interview 29

We explored patients’ and relatives’ help-seeking behaviour at stroke symptom onset using quantitative questionnaires and qualitative interviews. Our hypothesis that having stroke awareness would be positively associated with appropriate help-seeking behaviour was partially supported by quantitative and qualitative data, which confirmed and contradicted each other and sometimes provided potential explanations for apparent inconsistencies, as we discuss below.

Summary and discussion of main findings

Qualitative findings around the impact of knowing stroke symptoms were found to be partially in discordance with quantitative findings. Specifically, questionnaires showed patients with knowledge of stroke symptoms to be more likely to recognise their symptoms as stroke and to arrive at hospital on time. In contrast, interviews showed many patients to not have recognized their symptoms as stroke, even when they knew of common stroke symptoms. Two patients explained that they did not feel ill and even that they felt comfortable. This was confirmed by a former stroke patient in the Patient Council who reported not linking their general knowledge to their acute experience and inexplicably feeling safe and seeing everything through rose-tinted glasses. While the literature shows that lack of pain or perceived symptom severity can contribute to a diminished feeling of urgency, we were not able to find published descriptions of these feelings of comfort or safety [ 16 , 27 , 28 , 29 ].

Regarding the importance of familiarity with information campaigns , our qualitative and quantitative findings complemented each other. While questionnaires showed that patients familiar with campaigns were more likely to recognise their stroke, interviewed patients reported other information sources. Findings from the published literature show a variety of results in terms the impact of stroke information campaigns, e.g. reporting (partial) effectiveness [ 7 , 8 , 10 ] but also rather limited impact [ 6 , 9 ]. Notably, in our study, patient reporting of prior stroke information sometimes appeared inconsistent, e.g. when patients later spoke about a relative with stroke. This suggests that patients have better recall of some types of information than others [ 28 ]. It may also be suggestive of individual patient characteristics contributing to avoidance behaviour. Moloczij et al. called this the desire to “[maintain] a sense of normalcy”, describing several strategies used by patients to support their decision not to take any action, including denial, minimisation of symptoms, and compensating or adapting [ 16 ]. Wang et al. use descriptors such as “hesitating and puzzling” and “doubting – it may only be a minor problem” to describe this process experienced by stroke patients before initiating help-seeking [ 30 ].

Partial discordance was also found for previous strokes . While questionnaires showed patients with one or more previous strokes more likely to recognise their current symptoms as stroke, none of the five patients in the qualitative sample had recognised their current stroke. In their literature review of factors influence prehospital delay and stroke knowledge, Teuschl and Brainin (2010) find that only few studies report shorter time delays or better stroke knowledge in persons having suffered a previous stroke [ 27 ]. While silent (previous) infarctions may explain some of these instances, one patient who actively experienced their previous stroke reported avoidance behaviour before the second stroke. This was also reflected in Mackintosh et al.’s study of why people do (not) immediately contact emergency services, including several patients who recognised their second stroke but did not take action [ 28 ]. This observation was discussed in the Patient Council whose patient representatives showed surprise at the apparent lack of impact of previous stroke experiences. It was discussed whether stroke patients may not perceive themselves as living with a long-term condition requiring ongoing vigilance, but instead an isolated and completed incident.

Finally, qualitative and quantitative data were found to overlap and expand each other for knowing other stroke patients and having discussed the disease stroke . Interviews provided additional insights into possible reasons for when patients did not relate to others’ experiences and showed the importance of relatives knowing other stroke patients. Questionnaires showed no significant associations between knowing other stroke patients and the four dimensions of appropriate help-seeking behaviour, but patients who had discussed symptoms with other stroke patients were found to be more likely to recognise their stroke and to arrive at hospital on time. Again, there appeared to be inconsistencies in the interviews, with patients forgetting and then remembering knowing someone with stroke, and with many patients not relating others’ stroke experiences to their own situation. In contrast, several relatives identified knowing other stroke patients as the specific reason why they recognized the patient’s stroke and knew how to react. The importance of bystander involvement was explored by Mellon et al., identifying symptom recognition and help-seeking by witnesses as critical for a fast response [ 31 ]. For instance, Geffner et al. found that the decision to seek medical help was taken by patients in only 20.4% of cases [ 32 ]. Iverson et al. also found the presence of a bystander at symptom onset to be associated with appropriate help-seeking [ 15 ]. However, other qualitative findings are more nuanced, e.g. with Mc Sharry et al. reporting actions taken by others as having the potential to override patients’ own identification of symptoms and Moloczij et al. finding that sometimes the presence of another person contributed to delayed help-seeking, while at other times facilitating contact with medical services [ 16 , 29 ]. In addition to patients’ and relatives’ own behaviour and decisions, studies also show the importance of system factors, such as inefficient pre-hospital triage for treatment delay [ 33 ].

Strengths and limitations

As data collection was prepared and conducted independently, it was not always perfectly matched. One example of this is the fact that the rural-urban divide was not considered in detail in the qualitative data collection. This means that potentially important qualitative explanations of quantitative findings related to rural vs. urban differences were not explored in the current study, such as potential differences in information access, transport time or time-to-access to emergency services. Moreover, as is appropriate for qualitative interviews, prompting for more detailed information depended on the specific context and was therefore not feasible for all interviewees and all sub-questions. In the questionnaires, patients were asked about prior knowledge of stroke systems after they had their stroke. However, since it was completed on the day itself or day one after treatment, there would not have been much time for extended patient education. Additionally, the quantitative questionnaire was analysed with a pre-defined analysis plan and was collected over a (pre-defined) time period of six months. However, no power or sensitivity analysis was conducted in advance. Finally, our qualitative sample showed very good recovery, which probably affected the range of experiences and reactions covered in the interviews. One might assume that this overrepresentation of good outcomes could suggest a similar overrepresentation of study participants who “acted correctly”. However, given the importance of luck, bystander help, patients’ physical incapability to react and additional factors other than informed decision-making reported in this study, our results indicate that caution is warranted when interpreting good outcomes or arrival inside the time-window as proxies for having acted quickly or correctly (and vice versa). The main strengths of this study are its two-site design covering hospitals in urban and rural areas with differences in acute stroke treatment options, ensuring good external validity for Germany and countries covering larger geographical areas, its mixed methods approach allowing for integration of findings and generation of new perspectives of inquiry, and the involvement of patient representatives in the study preparation and conclusion.

Recommendations

As quantitative and qualitative findings sometimes seemed contradictory, we recommend that future studies collect data from one patient sample (instead of two separate samples, as here), allowing for direct back-and-forth iterations.As qualitative interviews pointed towards relevant inconsistencies in patient reporting, e.g. of prior stroke knowledge even with regard to close family members, it might be worth re-examining the reliability of common quantitative measures of stroke awareness and help-seeking behaviour where these inconsistencies would remain hidden and potentially incorrect. Following the Patient Council discussions, future research may investigate the “comfortable lull” reported by two patients from the study sample and one patient from the Council. If found in more instances, this could contribute to patients not recognizing a situation as highly problematic and requiring urgent action. In terms of practice recommendations, a more family- or community-based approach to stroke information provision may be helpful, emphasising the opportunity to be a loved one’s saviour. This could lessen the impact of avoidance behaviour and increase the positive impact of the presence of a family member on the decision-making process. This may necessitate critical discussions of whether and how relatives should be able to override patient preferences for delayed or no help-seeking behaviour, especially when the patient’s capacity for decision-making is impaired. As many patients seemed unable to apply general knowledge of stroke symptoms in the acute situation, we suggest exploring an example-based approach to risk communication. Specific situational examples (e.g. lying on the floor in the middle of the night or falling down without knowing why) may be a more accessible source of information compared to paresis of the arms or legs. To provide this type of information in the most appropriate way to future patients and their relatives, it seems relevant to involve former stroke patients in the preparation and provision of these informational resources.

Our study provides insights into the complexity of a decision-making process that is influenced by certain factors, but not others – e.g. a previous stroke makes it more likely that a patient recognises their symptoms as stroke, but not that they call for help without hesitation or arrive at the hospital on time. Interviews with patients and relatives provided in-depth insights into these seemingly contradictory findings, e.g. suggesting processes of denial or the inability to translate abstract knowledge into correct actions. We therefore recommend to address relatives as potential saviours of loved ones, increased use of specific situational examples (e.g. lying on the bathroom floor) and the involvement of patient representatives in the preparation of informational resources and campaigns.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. This excludes interview transcripts as ethics requirements to ensure confidentiality do not allow for data sharing outside the research team.

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Acknowledgements

The authors thank all study participants for their participation and valuable contribution to this study. For the publication fee, we acknowledge financial support by Heidelberg University.

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Matthias Ungerer and Christoph Gumbinger contributed equally to this work.

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Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

Loraine Busetto, Christina Stang, Melek Sert, Johanna Hoffmann, Jan Purrucker, Wolfgang Wick, Matthias Ungerer & Christoph Gumbinger

Institute of Medical Virology, Goethe University Frankfurt, University Hospital, Paul-Ehrlich-Str. 40, 60590, Frankfurt am Main, Germany

Loraine Busetto

Department of Paraplegia, Heidelberg University Hospital, Schlierbacher Landstraße 200a, 69118, Heidelberg, Germany

Franziska Herzog

Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

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LB drafted the manuscript, conceptualised the overall mixed methods study design, and was responsible for the qualitative study design including qualitative data collection and analysis. CS, FH, MS and JH conducted the qualitative interviews and contributed significantly to qualitative data analysis. JP, FS, MB and WW provided medical expertise, contributed to quantitative analysis and revised the manuscript. MU conducted the quantitative analysis and contributed to the mixed methods analysis. CG had a supervisory role, contributed significantly to quantitative analysis and mixed methods design and analysis and relevantly revised different manuscript versions.

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Busetto, L., Stang, C., Herzog, F. et al. “I didn’t even wonder why I was on the floor” – mixed methods exploration of stroke awareness and help-seeking behaviour at stroke symptom onset. BMC Health Serv Res 24 , 880 (2024). https://doi.org/10.1186/s12913-024-11276-6

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Student council election process and effectiveness of student councils in public secondary schools in siaya county, kenya, phoestine naliaka simiyu.

The Ministry of Education in Kenya introduced the Student Council Policy in 2009 to enhance student participation in school governance and make student leadership more participatory. This was to help reduce the cases of student unrest in schools. However, even with student councils in place, schools still experience cases of student unrest among other indiscipline cases. Concerns have therefore been raised over the effectiveness of student councils in their role performance, which necessitates such research. This study sought to assess the influence of the student council leaders’ election process on student councils’ effectiveness with clear focus on establishing how student council elections were conducted in schools; the extent to which the election process influenced student council’s effectiveness and suggest some of the measures that could be used to enhance the effectiveness of Student Councils (SCs) among public boys’ boarding secondary schools in Siaya County, Kenya. The sample included 14 deputy principals, 178 student council leaders, and the County Director of Education (CDE). The study employed a correlational research design using the convergent parallel mixed methods approach with purposive sampling to select respondents. Interview schedules and questionnaires were used to collect data. The quantitative analysis was done using descriptive and inferential statistics. Qualitative data was presented thematically according to the objectives and presented via narration and word verbatim. The analysis established that most of the public secondary schools had blended student councils with some of the council members elected while majority of them were appointed by the school administration. The study also found out that there was a strong statistically significant positive relationship between the student council election process and effectiveness of student councils. The study recommended the need for schools to fully implement the SC policy as designed by the MoE; that students should be allowed to elect their leaders with minimal interference by the school administration; and the Ministry of Education should have elaborate guidelines on the management of student council elections in public secondary schools in Kenya.

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