How to locate yourself (and others!) in the research process: The role of positionality
Bibliographic record
Abstract
Recently, a colleague shared a manuscript review that she was struggling with. This colleague—a long-time emergency physician—had studied physicians' experiences of moral distress in the emergency room (ER) during the pandemic. Many of us would believe her own experiences as an ER physician make her ideal for this exploration. However, the reviewers held a different perspective about the role of the researcher in the research process. Their critiques included comments such as, ‘Should you be studying the ER context? Aren't you biased?’. While questions like these can be frustrating, they capture a pressing need to better explore the concept of positionality in qualitative research. Embedded in such reviews is a desire to better understand who the researcher is and how they shaped the results. The challenge for Health Professions Education Research (HPER) is that to-date, positionality has either been approached like a checklist, through the lens of ‘bias’, or not at all. The problem with this approach is threefold. First, it leaves readers to infer and assess a component of rigour on their own, making it difficult to learn how to apply these principles in their own work. Second, it can generate misleading questions about implicit bias, inter-rater reliability and so on, that can undermine the coherence between research design and communication of results. Finally, it can generate reflexive statements that merely list identity categories without articulating why this is meaningful and how it impacted the study. Below, we unpack the concept of positionality and its relevance to each phase of qualitative research. In doing so, we make the connection between positionality and reflexivity clearer and provide researchers with practical questions to guide them through this process. Positionality is dynamic, contextual and informed by broader power relations. One's positionality can shift over time and place, within an institution and in relation to different research projects.5 Commonly in HPER, positionality has often been conflated with bias, and researchers have been asked to account for how they mitigated its effects. However, we posit that mitigating the researcher's perspective (i.e., bias) is not as productive as asking, How did one's positionality shape the study and what were the affordances and limitations of the study because of it? If producing rigorous research is the goal, using positionality to engage in deeper reflexive practice—questioning how power shapes one's knowledge, assumptions, experiences and position in the world6—may be more fruitful and meaningful. Positionality, then, is a critical component of reflexivity, but the terms should not be conflated.5 Positionality is a tool to understand who we are in relation to our research/institutions/social worlds, and reflexivity asks us to critically reflect on how our positionality shaped knowledge production.5-7 Positionality can help produce more ethical work.7 For instance, you may realise you are an outsider to a particular community, and an advisory group would help ensure that a community's voice is reflected throughout the research process. Indigenous communities have created very clear guidelines to this effect, particularly for researchers who are not community members.8 Positionality can improve the work's coherence. Our research choices do not suddenly appear nor can they be entirely explained in empiric terms. They are often a combination of who we (and our teams) are, who participants are and what scholarly approaches we choose.2, 5 When we are clear on why we (and not other people) are studying a particular topic, how we are approaching knowledge production and how we are making meaning from the data, we can offer a more coherent research narrative for participants and readers. Finally, positionality can help us answer questions we commonly receive in the field. For instance, participants sometimes ask, ‘Who are you? Why are you doing this work? Why should I trust you?’ These questions are not only about scholarly integrity, but also about our positionality. Reviewers ask these same questions, as in the example we opened with. Engaging in this inquiry from the early stages of research design may bring more rigour and authenticity to the research process. Below, we provide an outline for how positionality informs each phase of research and some questions to guide reflexivity. There are multiple tools for mapping positionality. Jacobson and Mustafa's tool, for example, helps researchers map aspects of their social identity reflect on how they interact with broader power relations.2 It includes three tiers: mapping one's social identity (i.e., gender, citizenship, [dis]ability, and race), mapping how identity impacts one's life and mapping the emotions tied to identity. As an example, EF filled out an adapted version of this map when she joined a team exploring experiences of pregnant women with higher BMIs (Figure 1). While the map has the aspects of identity in discrete boxes, it is important to note that these pieces of identity often overlap and shape one another (intersectionality).9 This exercise helped EF reflect on how BMI shaped women's healthcare experiences differently (i.e., stigma) and on HCP's intrinsic stereotypes about people with larger BMIs. This reflection helped EF craft the interview questions encouraging participants to describe their body mass in their own terms. To explore other researcher and participant characteristics, such as profession, specialty or disciplinary training, the COREQ checklist can be used.10 Embedded in this tool is an ‘acknowledgment of the multiple roles and positions that researchers and participants bring to the research process’.11 While listing these roles and positions is important, positionality recognises that research is relational and reminds us that there are nuances to the researcher/participant relationship.5 For example, a nurse's positionality will have a different impact/meaning depending on the study's context—for example, a study about nurse/patient relationships versus nurse/surgeon relationships.12 These features reflect broader power relations that impact how we experience the world and how we ‘know what we know’. Tables 1 and 2 offer some questions you may reflect on as you engage in study design to provide a more structured approach to the reflexive process. Who will be collecting the data? Are they an insider or outsider (and in what ways)? Are there shared values/experiences between researcher/participant? Are there differing values/experiences? What are the power dynamics between participants/researcher? Given the above, what preparation for the field is needed? What are the affordances and limitations of the person collecting the data? Whose voices are represented in the sample? Whose are missing? Why? Are there examples where positionality appeared relevant in the research process? ‘How did I use my positionality in different spaces?’13 ‘How are race, gender and/or class made meaningful in the participant/researcher relationship?’5 Regardless of what one opts to include, we urge researchers to think through the positionality of themselves, their research teams and the participants to consider how it shapes their research approach (see Table 3, e.g.). Positionality is of particular importance during data analysis, yet we rarely discuss it in detail. Often we read, ‘the research team brought diverse perspectives, and met frequently to develop consensus on themes’. While our teams may develop consensus on themes, the meaning we make of them and how we present them are deeply connected to our positionality.2, 5 To make these connections more explicit, research teams may employ a variety of strategies. Team members could complete a positionality map prior to data analysis to reflect on perspectives each may bring to the project. Alternatively, team members could craft reflexivity statements after data analysis to better understand how they interpreted themes and inferred meaning from them. Table 4 offers some questions to guide this process (see Corlett's and Mavin's work for more).7 What were your affective responses to participant's experiences/story/data? What informed these responses? When did you relate to participant data? When did you diverge/struggle to relate? What meaning did you make/ascribe to particular themes/quotes? What informed these interpretations? What did you expect to see in the data? What assumptions did you have before reviewing the data? What informed those expectations/assumptions? What surprised you? To illustrate the role of positionality in data analysis, Table 5 provides an example based on EF's work. EFs example is helpful but also highlights a unique challenge: How do we meaningfully convey the impact of positionality on our research? And furthermore, how much of ourselves—and our personal lives—do we need to share in our manuscripts? Each project and team must decide for themselves where the balance lies. Sharing personal characteristics during data analysis meetings may be appropriate but less appropriate during the crafting of a manuscript. In other cases, such as the one EF describes, it may feel both relevant and scholarly. What is important for readers is not necessarily that every aspect of a person's positionality is described, but rather, that the research team has considered the role of positionality to engage in their reflexive practice. In other words, that the ‘who’ they are, and the impact this has, was factored into the research process. This can often be captured in a manuscript's reflexive (or ‘reflexivity’) statement. As our HPER community continues to grapple with positionality, we hope this paper has offered some insights into how to map one's positionality and convey its impact on one's work in a manner that moves beyond listing identity characteristics. Instead, researchers can critically reflect on what positionality means for data collection and subsequent analysis by perhaps, using some of the tools we have provided to encourage deeper reflexive practice. With a clearer sense of the role of positionality throughout knowledge production, teams can craft more coherent statements about study goals, the logic behind the design and data analysis choices. Furthermore, they may be able to provide more thoughtful statements on study limitations. We recognise that word limits are a serious contributor to why positionality and reflexivity are often explicitly missing, or topical, in research manuscripts, oftentimes only one or two sentences long. Our current approach to reflexivity and positionality must move beyond checklists. Publishers need to recognise the significance of these components of qualitative rigour and create the necessary space for researchers to address them.2 As a community of scholars, we should continue to reflect on the role of positionality in our own work, and how we engage with it as a principle of rigour. This may even include debating how and when it is valuable and what we should include in our manuscripts.15 In doing so, we hopefully can make the embedded, and hidden components of our work, more visible and, thus, meaningful. Emily Field: Conceptualization; writing—original draft. Erin Kennedy: Conceptualization; writing—original draft. Sayra Cristancho: Conceptualization; writing—original draft. The authors have no acknowledgement to disclose. The authors have no conflict of interest to disclose. The authors have no ethical statement to declare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".