Abstract
Qualitative story completion (QSC) is an innovative research method that offers researchers a range of unique opportunities for generating and analysing data. Participants are asked to write a ‘story’ in response to a hypothetical ‘story stem’, often in the third-person and involving fictional characters, rather than reporting on their direct experiences. QSC is being developed and increasingly taken up by researchers working across a range of fields; but it has been little used in health research, especially in the fields of nursing, health services research, medicine, and allied health. This means that health researchers have few examples to draw on when considering what QSC can offer them and how to rigorously design, conduct, and report a QSC study within health-related fields. We aim to address this gap and contribute to existing QSC literature by promoting increased use of QSC by health researchers and supporting them to produce rigorous QSC research. We outline three case examples illustrating how we have used QSC to conduct multidisciplinary health research relevant to nursing, medicine and nutrition. Drawing on these case examples, we reflect on challenges that we encountered, describe decision-making processes, and offer recommendations for conducting rigorous health research using QSC.
Keywords
Introduction
Qualitative story completion (QSC) is a research method offering a range of unique opportunities for generating and analysing data. QSC has been used primarily within psychology and related disciplines to explore topics such as gender, sexuality, and the body, often from a social constructionist lens (e.g. Clarke & Braun, 2019; Clarke et al., 2015; Frith, 2013; Hayfield & Wood, 2019; Jennings et al., 2019; Moore, 1995; Tischner, 2019). More recently, researchers have begun taking up QSC in other areas, including sports and exercise (Williams et al., 2021) and education research (Gravett, 2019), and adopting alternate philosophical frameworks such as critical realism or pragmatism (see Moller et al., 2021). Yet QSC remains little used in health research. The method has been used to explore health-related topics broadly, for example, psychological health or service provision (Moller & Tischner, 2019; Scholz, Bocking et al., 2020; Shah Beckley & Clarke, 2021; Shah-Beckley et al., 2020; Vaughan et al., 2022), healthy eating or weight loss (McDonald & Braun, 2022; Tischner, 2019), and health information seeking (Lupton, 2021a). There are very few examples of QSC research within the fields of nursing, medicine, health service research, and allied health (see Coultas et al., 2020; Diniz et al., 2020). This means that there are few empirical examples for researchers to draw on when considering how to rigorously design, conduct, and report QSC research within these health-related fields.
The aim of this article is two-fold. Broadly, we invite researchers working in health fields to consider how QSC can be taken up and what it can offer as part of their methodological ‘toolkits’. More specifically, we aim to support researchers in their approach to using QSC to produce rigorous health research. The article will be useful for researchers who are new to QSC and want to explore how this method can be used; and for researchers who might have encountered or used QSC before and are thinking through some of the design complexities and challenges for future research. This article builds on the excellent methodological introductions to QSC (e.g. Braun et al., 2019; Clarke et al., 2019; Moller et al., 2021; Smith et al., 2022) produced by the Story Completion Research Group (The University of Auckland, n.d.) and others by providing a focused and illustrated discussion of QSC within the health space. We begin by briefly introducing QSC and outlining why researchers who are conducting health research might choose this method. We then present three case examples to illustrate how we have used QSC in multidisciplinary health research. Drawing on the case examples, we reflect on challenges we encountered and overcame, describe key decision-making processes, and offer recommendations for conducting rigorous health research using QSC.
What is Qualitative Story Completion and What Does it Offer?
In QSC, participants write a story in response to a ‘story stem’, or hypothetical scenario created by the researcher (Clarke et al., 2019). Story stems are designed to provide enough information to elicit a response related to the topic of interest; but are intentionally ambiguous, so that many different responses are possible. Participants are typically asked to write in the third-person about fictional characters, but first-person responses are also possible (Braun et al., 2019; Jones et al., 2020). Researchers can choose from a range of qualitative analytic methods to analyse story response data, depending on their broader methodological design (Moller et al., 2021). Researchers have commonly used reflexive thematic analysis to generate patterns across the dataset (i.e. a ‘horizontal’ analysis: Braun et al., 2019, p. 1491; Vaughan et al., 2022). There are also opportunities to use narrative analytic approaches to explore the ‘vertical’ structure and patterning of participant responses (i.e. examining how stories unfold: Braun et al., 2019, p. 1491; Williams et al., 2021). The data generated through QSC are generally understood as providing access to social meaning-making tools and resources, rather than participants’ direct experiences and perceptions (Clarke et al., 2019). These data allow researchers to examine the broader social, cultural, and professional understandings and ideas available to participants in relation to the story stem topic or scenario (see Moller et al., 2021 for elaborated discussion). Alternatively, researchers can interpret data as revealing participants’ direct experiences or psychological reality (see Moller et al., 2021); but, within this approach, QSC might not be advantageous over direct self-report methods such as interviews or qualitative surveys, except when researching sensitive topics.
As a method, QSC offers many benefits for researchers across fields (Braun et al., 2019; Clarke et al., 2019). These include the opportunities to generate a wide range of participant responses, including socially undesirable responses, and explore sensitive topics in safe ways. Unusually for a qualitative method, QSC allows researchers to design robust comparative studies (Braun et al., 2019; Moller et al., 2021). Researchers can, for example, compare participant responses across different story stems in which a detail is changed, or compare responses to the same stems across different participant groups. QSC is a reasonably economical qualitative method (Braun et al., 2019; Clarke et al., 2019), both in terms of researcher time and resources and participant burden (depending on study design). The method also encourages imagination and creativity for both participants and researchers themselves (Braun et al., 2019; Clarke et al., 2019; Smith, 2019). Related to this, even though QSC is a novel method itself, it presents a range of possibilities for further methodological innovation (e.g. Braun et al., 2019). There are already examples this, including innovative uses of QSC in mixed method studies (Diniz et al., 2020; Hayfield & Wood, 2019) and with other modes of creative inquiry (Lupton, 2021b), and adaption of the method with group-based oral responses to story stems (Coultas et al., 2020).
QSC offers a range of exciting opportunities for health researchers to examine phenomena in new ways. Compared to more conventional and commonly used qualitative methods, such as observations, interviews, and focus groups, QSC can provide a unique way to include and understand stakeholder perceptions, understandings, or sense-making in a way that moves beyond drawing solely on direct experience. This provides an avenue to examine sensitive topics, compare sense-making across different stakeholder groups, and explore possibilities for healthcare provision even if those possibilities have not been experienced directly by participants themselves. The insights generated through QSC studies can also be used to inform pragmatic recommendations for improving clinical practice and education; this is often an important aim in health research involving practice-based disciplines such as allied health and nursing (Leach & Tucker, 2018; Sandelowski, 2004; but also see Grypdonck, 2006). The relevance of QSC for generating practice-relevant guidance might not be immediately apparent to researchers due to the method’s focus on meaning-making or conceptualisations rather than lived experience, and the use of hypothetical stories involving fictional characters. As noted in the Introduction, there are also few worked examples demonstrating how the method can be used in health research within fields such as nursing, medicine, health service research, and allied health. To illustrate what QSC can offer health researchers, in the next section we discuss three case examples of how we have used the method productively in our own multidisciplinary health research.
Qualitative Story Completion in Health Research: Case Examples
Overview of Three Case Examples Using Qualitative Story Completion in Health Research.
aWe report the median because the data were not normally distributed.
Case Example 1
The Caregiving Relationships Study (ongoing) explored how participants from three stakeholder groups (nurses, informal carers, and adult healthcare consumers) conceptualise or understand caregiving relationships within healthcare encounters. Caregiving relationships between healthcare professionals and patients are understood as centrally important for quality healthcare, but theoretical conceptualisations of these relationships lack clarity within and across disciplines. These theoretical conceptualisations have also prioritised researcher perspectives, so nurses’, consumers’, and carers’ perspectives have been largely absent from empirical and theoretical work in this space. When participant perspectives have been included, there has been a focus on their direct experiences rather than on exploring the kinds of social, cultural, and professional understandings available to participants to understand caregiving relationships or think about what an ‘ideal’ relationship should look like. Further, existing empirical and theoretical work has generally focused on dyadic contexts – relationships between one healthcare provider and one patient – with little consideration that relationships often involve multiple actors and take place within wider care networks. QSC was seen an ideal method for overcoming these limitations.
Using QSC we sought to explore how professional and lay participants conceptualise a ‘good’ or ‘ideal’ caregiving relationship within a healthcare encounter, rather than participants’ actual experiences of seeking or providing healthcare. We also examined how participants’ conceptualisations varied across stakeholder groups, and across dyadic versus triadic relational contexts. Participants in each stakeholder group were randomly allocated to receive a story stem involving two or three fictional characters (dyadic or triadic condition, respectively). In both conditions, participants responded to the initial stem, and then to several follow-up prompts designed to elicit further detail about the story and the characters’ experiences (see Supplementary Material). Insights from this study will contribute current conceptualisations of caregiving relationships to inform rigorous intervention design, implementation strategies and methods of evaluation (both in education and clinical practice) to improve caregiving relationships.
Case Example 2
The COVID-19 and Family Life study (under review) was a collaboration between nutrition, physical activity, and psychology researchers who used QSC to explore how Australian caregivers made sense of the COVID-19 pandemic and its impacts on caregivers’ behaviours relating to self-care and care for others. In addition to collecting data on their individual experiences of the pandemic, we were uniquely interested in how participants perceived the impact of pandemic-related restrictions and lockdowns on families’ abilities to manage sleep/personal care, housework, caregiving, leisure, and paid work. This was a multi-method study involving the collection of quantitative data about time use and diet. We chose to also use QSC because we were interested in going beyond direct family experiences to explore how the pandemic might have disrupted taken-for-granted routines and structures that have upheld and maintained family life, health, and social behaviours. Upon completing the quantitative component of the study, participants were randomised to respond to one of three story stems grounded within the context of COVID-19 lockdowns and restrictions. Stems focused on how families navigated and managed household tasks; how families navigated and managed children being unable to see friends, extended family, or attend school, extra-curricular sports and activities in-person; and how families navigated caregiving and working from home, respectively. Through using QSC and triangulating the results with the quantitative data, we were able to identify the importance of caregiver relationship quality for family health and wellbeing.
Case Example 3
The Ageism in End-of-life Care in COVID-19 study (under review) explored how people made sense of age in the context of triage for COVID-19. Prior to this story completion study, the research team were involved in community consultations during the early stages of the pandemic to develop the triage processes for ventilators. Many of the discussions reflected media stories about older people (and other vulnerable groups) being discriminated by health systems globally, and people were understandably worried about the way they might be triaged should they need care as older adults during the pandemic (Scholz, Kirk et al., 2020). QSC was helpful to explore people’s sense-making about age and medical triage and prioritisation, as the rhetoric in news and social media had been feeding social divisiveness (e.g. with COVID-19 being called a ‘boomer remover’ and other ageist tropes). Story completion provided opportunities for participants to create stories drawing on or transcending these tropes. Using QSC allowed participants to have those discussions in safer ways: The hypothetical context of QSC yielded data about people’s fears and desires about age and triage, while minimising potentially traumatic conversations. The method was particularly useful in this project for looking at the ways that ageist discourses shaped participants’ stories about valuing others’ lives. We designed story stems about physicians triaging old and young patients for ventilators in the context of the pandemic. We collected stories from young adults online and used a hybrid of online and in-person data collection for older adults. One participant (a man in his 70s and thus in our older adult age group for the study) championed our data collection method, inviting us to bring hard copies along to some of his community and social gatherings and asking his friends to complete stories. These older participants also discussed their stories with each other and the researchers afterwards, and although we did not capture those conversations as part of data collection, we reflected on how useful it would have been to do so.
Considerations, Challenges, and Opportunities for Qualitative Story Completion in Health Research
In this section, we draw on our case examples to discuss some major considerations, challenges, and opportunities that we identified through learning to use QSC in multidisciplinary health research. This discussion is organised into three main sections, each with a key recommendations summary: (1) Crafting story stems; (2) Generating the dataset; and (3) Interpreting and reporting data(set) quality and size. These topics provide an overarching structure for our discussion, not a linear or stepped process for designing and conducting QSC research. Like much qualitative research, using QSC is an iterative process both in terms of designing and generating the dataset and analysing the data. At the end of each section, we outline key recommendations that are drawn together from the wider QSC literature and our own experiences of learning to use the method. By providing this illustrated discussion and specific recommendations, we aim to support researchers to think through the complexities of using QSC to conduct rigorous health research.
Crafting Story Stems
One of the early considerations when using QSC is crafting the story stems that will drive your data collection (Braun et al., 2019; Moller et al., 2021). While there is not one right way to craft stems, there is often minimal discussion provided in empirical QSC studies about how authors produced their stems; this is especially the case in health research where these has been little QSC research conducted. We identified some key considerations, outlined below, that help to shape the kind of data you will be able to collect.
From a Research Question to Story Stems
In our experience, crafting a story stem involves discussions with collaborators about how to move from a broader research question to developing story stems (also see Vaughan et al., 2022). For instance, in case example 3, the research question evolved from our experiences in a large collaboration with consumer, carer, and community groups designed to produce the principles of triage in the context of COVID-19 in one Australian territory (Scholz, Kirk et al., 2020). A source of contention in these discussions was whether people should be triaged by age. The final triage implemented as a result of the collaboration was based on human rights, meaning age was not a deciding factor; but there were still tensions related to different opinions about the role of age in COVID-19, perhaps exacerbated by mass media at the time (Meisner, 2021). A story stem was developed based on the idea of ICU physicians needing to decide whether to provide the last ventilator to a young or an older adult, to allow stories to provide details on how people thought that decision might or should be made.
Other considerations requiring some thought and piloting (discussed below) are the amount of detail provided in the stem, and the number of stems used. Enough context and information need to be provided to ensure participants complete their stories along the lines of your broader research question (Braun et al., 2019). However, having story stems ‘open’ enough for participants to engage in creative thinking is one of the strengths of this approach. In the wider literature, it is common for participants to complete a single stem, but there are examples of up to six stems per participant (e.g. Gavin, 2005). A key consideration here is that QSC already asks participants to engage in creative labour and they might not be motivated to complete more than a single stem; but fewer stems can mean that each participant will generate less data. One way we have overcome this is by using one stem per participant but asking additional follow-up questions about the original stem (see Supplementary Material for stem structure examples).
Pseudonyms
QSC studies tend to select names for fictitious characters that could apply to people of any gender (noting of course that this differs by cultural and language norms). There are several QSC studies, including our own (case study 1), that use the pseudonyms Alex or Sam for this reason. Stems then often use gender neutral pronouns to refer to these characters or avoid introducing pronouns at all. Despite attempts to keep gender open, there does seem to be a tendency for gender norms to influence participants’ gendering of characters. For instance, in Scholz, Bocking et al. (2020) all but one participant gendered fictional doctor characters as men. Additional demographic variables are also important to consider when ‘naming’ fictional characters. For instance, in case example 3, which explored ageism, the pseudonym ‘Charlie’ was chosen as a name that could refer to people of any age. This decision was based on data about the name showing a long history of popularity of the name for babies regardless of sex (Everything Birthday, n.d.).
While we appreciate the lengths authors (including ourselves) have gone to try to select ‘neutral’ names where this is relevant for their study design, we think it is important to remember that no name can be truly neutral. Another consideration with naming is that participants can get names confused in their responses to stems where there are multiple named characters, as we found in case example 1 for stems with three characters. An advantage of referring to characters by name, and not just their role, is to maintain the story-like nature of the stem and engage participants in the task (Braun et al., 2019). A potential future direction could be to allow participants to create their own name for the character(s). This could minimise researchers’ influence on participants’ perceptions of a specific name, and help participants to remember ‘who’s who’ in stems with multiple characters.
Piloting
Participants are likely to interpret story stems in varied ways, but we have found that piloting the stems is a useful step to ensure that the stems make sense (piloting is also recommended in the wider literature: Braun et al., 2019; Moller et al., 2021). It may be particularly helpful to pilot story stems with people with lived experience of the phenomenon being explored. For example, in case example 3, we piloted the stems with members of the local Council on the Ageing who were able to provide relevant advice on whether our stems might be implicitly biasing participants in any given way.
Key Recommendations
• Moving from a research question to a story stem that will help you answer that question requires careful thought. Story stems are likely to be specific examples of a broader concept that you are interested in; piloting the stems allows you to ensure you are capturing enough information without limiting participants’ creativity. • Specifically developing or piloting story stems together with people with lived experience of the phenomenon in question can help to ensure good face validity and alignment between research question, story stem, and lived experience. • There are perhaps more considerations to choosing names for your story stem character(s) than meets the eye – consider what participants might associate with names (such as gender, age, or other demographic identities) and how that could shape your data.
Generating the Dataset
A key element of QSC to consider is how you will generate your dataset. We identified several key considerations based on our experience of using this method.
Mode of Data Collection
Across our case examples, data were collected in person, online, and via telephone. There is no right way to collect QSC data and often it is guided by pragmatic reasons such as time and resources. In case example 2, the QSC task was designed in an online survey. However, it was embedded in a mixed method study where telephone interviews were completed prior to participants being provided the QSC survey link. This meant that rapport was developed with participants, a verbal explanation of the purpose and expectations of the QSC task were communicated, and participants were able to ask questions prior to commencing the task online. In contrast, case example 1 was conducted entirely via online survey accessed through a link embedded in recruitment advertisements. The ability for researchers to collect QSC data online has several advantages including being economical (Braun et al., 2019) and facilitating participant recruitment across geographical regions (provided people can access technology). It also means that participants can complete the task at a time and place that suits them, without the presence of a researcher, and at their own pace, which might help to facilitate creativity and deep engagement with the task. There can be disadvantages with conducting QSC solely online, too. QSC requires something very different from participants compared to more traditional data collection methods, and participants might not grasp that the researcher is interested in receiving a hypothetical story rather than a report of participants’ direct experience or opinion. Therefore, the way that you will communicate about the study and provide instructions to participants needs to be considered alongside your specific mode of data collection, and we discuss this next.
Participant Instructions
The importance of ensuring that participants understand what is required of them and the expectations of the stories is commonly emphasised by researchers writing about QSC (e.g. Braun et al., 2019; Moller et al., 2021). Providing a clear description of the research process and task instructions helps to facilitate participants’ comfort and gather meaningful data (Gravett, 2019). In our experience, communication and instructions to participants need to be tailored to the mode of data collection (e.g. in person, online survey, part of a mixed methods study). For example, in case example 3, some data were collected in person. Facilitating the task in person allowed for verbal instructions and guidance about what is expected of participants, and participants could ask questions and seek clarification. In comparison, in case example 1, which was conducted solely online, we needed to make sure that the written instructions were detailed and clear enough so that further explanation from a researcher was not needed. This required several iterations of piloting, both among the broader research team and directly with participants, to ‘get it right’.
Much like the need for piloting stems, it is also important to ensure that participant instructions are piloted (Moller et al., 2021). Piloting stems is required in every project to ensure these make sense and align to the research question. However, piloting instructions might not always be required if QSC has been used before and a research team has previous instructions that work well with the mode of data collection and can be used again with small modifications. Nevertheless, when using QSC for the first time, or if using a different mode of data collection, then it is important to pilot instructions to ensure that the chosen process will generate a rich and meaningful dataset.
Length of Participant Responses
You should also consider how you will communicate to participants how much they are expected to write in response to story stems and prompts. You can do this by communicating a minimum number of words or length of time participants should spend writing (e.g. 10-minutes: Braun et al., 2019). However, there is no clear guideline or consensus within QSC on a minimum length for story responses, making it difficult to justify or substantiate a minimum requirement. If you are providing participant reimbursement, however, it is important to ensure authentic participant engagement. One strategy, used across all our case examples, was to provide participants with reflective questions following the story stem to encourage participants to write responses longer than one-sentence and to provide additional detail (see Supplementary Material). Additionally, in case example 2, which used an online survey mode of collection, the response box was fixed at a size of ‘680px X 313px’ to encourage responses longer than a few words. Case example 1 also used an online survey mode and, following issues with fraudulent attempts to access participant reimbursement, we set a minimum character requirement based on the shortest authentic responses for the initial story stem (100-characters) and the follow-up prompts (50-characters).
Key Recommendations
• QSC can be difficult for participants to grasp. It is important to provide clear instructions detailing what QSC is and what is expected of participants, especially if using online survey modes. Telephone or in-person data collection allows participants to ask questions and seek clarification. • When using QSC for the first time, or via a different mode than before, it is important to pilot task instructions (in addition to stems) to ensure they are clear. • It is important to decide if (and how) you will communicate the length of response participants are to write. Use of follow-up questions or prompts to guide story development might assist participants in writing a meaningful story without focusing on length.
Interpreting and Reporting Data(set) Quality and Size
After collecting the QSC dataset, researchers need to determine the quality of the data and how to communicate or report information about the dataset (beyond the analysis itself) in the final report.
Data Quality
Even with careful study design and adequate piloting, you can expect at least some divergent 1 participant responses. These are responses that are fantastical in nature (e.g. feature monsters or magic, see Clarke et al., 2019; Moller et al., 2021) or otherwise ‘diverge from’ researcher expectations, task instructions, or most other participants’ story responses in style, form, or content. Using the research aim and question as guides, researchers need to decide whether the divergent responses are meaningful and should be included in the dataset for analysis (Smith et al., 2022).
A form of divergent responses that we encountered is when participants write in the first-person, despite task instructions to write in the third-person. This happened in case examples 1 and 3, and it was sometimes difficult to determine whether participants had written about themselves or were simply more comfortable writing a fictional story in first-person style. In case example 1, there were also clear instances where participants had written about a personal experience related to the story stem in either first- or third-person (they explicitly stated this, either in the story response itself or in the comment section at the end of the study). In both case examples, we decided that these divergent responses did ‘count’ as meaningful stories in relation to our broader research aims and questions, and so we included these in the dataset. Within QSC it is generally assumed that participants do draw on their own experiences, in addition to broader social and cultural ‘sense-making resources’, to inform their story responses (Clarke et al., 2019); but they are not constrained by those experiences because they are not asked to recount these. This means that divergent responses in the form of first-person stories or recounts of personal experiences can be considered useful, quality data insofar as they provide insights into participants’ meaning-making practices related to the topic (also see Vaughan et al., 2022). Alternatively, if most participants had written first-person responses outlining their own experiences when this was not the task, then we would need to consider whether our instructions had been clear enough and if QSC was the most appropriate method to explore our chosen topic with these stakeholders.
A form of divergent response that is uniquely relevant for health research is where health professional participants write responses more akin to clinical case formulations than ‘stories’. This has been reported by researchers who recruited psychotherapists (Shah Beckley & Clarke, 2021) and physicians (Smith et al., 2022); but interestingly, this was not our experience in case examples 1 and 3. It is possible that our use of follow-up prompts or questions helped clinicians to understand how to write a response that was more story-like (see Supplementary Material). Alternatively, clinicians from some professions might be more familiar or comfortable with writing creative, story-like responses than others (Smith et al., 2022). This is an interesting issue for health researchers to consider if they plan to use QSC with health professionals, and reinforces the importance of piloting both story stems and task instructions with different participant groups.
Reporting Sample Size
Another consideration is how to report the dataset and sample size within QSC studies. There is currently no consensus on how large samples should be or exactly what should be reported (Braun et al., 2019; Clarke et al., 2019): number of participants, number of story completions, length of stories, or some combination of these. Even among our case examples, the way we chose to communicate this information is varied (see Table 1). Choosing what information to report is influenced by research aims or goals, the underlying research approach, researcher preference, disciplinary norms, and the preferences or requirements of the journal where a manuscript is published. Because of this, we think it is not realistic to identify and achieve a strict consensus, but some guiding principles can help researchers to decide what information to report.
Reporting information about the size of the dataset beyond the total number of story responses – such as the minimum, maximum, and average length of responses – can be useful for communicating the quality or richness of the dataset and increasing transparency (e.g. Tracy & Hinrichs, 2017). We see this as an imperfect proxy for richness, like the common practice of reporting the average length (in minutes) of research interviews or focus groups. As QSC is a novel method, particularly within health research, reporting this information will help other researchers to make design decisions and evaluate their own datasets in relation to published QSC studies.
Of course, reporting information about dataset size risks reinforcing, or being misinterpreted through, a postpositivist assumption that quality is synonymous with quantity. This is a particular concern within health research contexts, where research underpinned by postpositivist assumptions of quality is common and where these values are sometimes applied to forms of qualitative research where they do not fit (Braun & Clarke, 2021; Carter & Little, 2007). Quantity does not always correspond with data richness or quality, so larger datasets will not always be richer than smaller ones and smaller datasets are not necessarily inadequate. This is generally accepted by researchers conducting qualitative research (e.g. Tracy & Hinrichs, 2017); but, as QSC becomes used more frequently in health research, researchers should carefully consider how they report the size of their datasets and how they communicate the usefulness of that information to avoid misinterpretation.
Key Recommendations
• Clear instructions cannot guarantee that all participants respond to story stems as expected – you can usually expect some divergent responses and will need to reflect on those in relation to your research aims and question to decide if those data are meaningful and can be analysed. • Currently, we do not know how acceptable QSC methods will be for different stakeholder groups or research topics within health contexts. When using QSC with new stakeholder groups in health contexts, take care to pilot well and engage stakeholders in the research design process where possible. • Reporting information about the size of the dataset in a way that is meaningfully aligned with your broader research approach can provide a sense of the richness or quality of your dataset; and support other researchers to design their own studies.
Concluding Comments
QSC is a method with rich opportunity for researchers in health-related fields. In this article, our aim has been to promote awareness and increased use of QSC by researchers working in fields such as nursing, health services research, medicine, and allied health. Researchers interested in exploring this method and using it in their research can and should draw on broader introductions and guides available (Braun et al., 2019; Clarke et al., 2019; Moller et al., 2021; Smith et al., 2022). This article contributes to this existing literature by illustrating how QSC can be used in health research and drawing on these experiences to provide demonstrated recommendations for designing and conducting rigorous QSC research in that context.
As qualitative research methods become more accepted as legitimate approaches to knowledge production across health fields, researchers who use qualitative methods are exploring new and innovative ways of conducting research and generating insightful knowledge claims (e.g. Jellema et al., 2019; West et al., 2022). When designed and conducted rigorously, QSC offers an innovative way of approaching health research that allows us to answer research questions and generate important insights not always possible using other research methods. We hope that this article will encourage researchers to consider and explore further what these possibilities might be.
Supplemental Material
Supplemental Material - Qualitative Story Completion: Opportunities and Considerations for Health Research
Supplementary Material for Qualitative Story Completion: Opportunities and Considerations for Health Research by Kristi Urry, Sarah Hunter, Rebecca Feo, and Brett Scholz in Qualitative Health Research.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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