Reflexive Thematic Analysis for Applied Qualitative Health Research
Bibliographic record
Abstract
Thematic analysis is a widely cited method for analyzing qualitative data. As a team of graduate students, we sought to explore methods of data analysis that were grounded in qualitative philosophies and aligned with our orientation as applied health researchers. We identified reflexive thematic analysis, developed by Braun and Clarke, as an interpretive method firmly situated within a qualitative paradigm that would also have broad applicability within a range of qualitative health research designs. In this approach to analysis, the subjectivity of the researcher is recognized and viewed not as problematic but instead valued as integral to the analysis process. We therefore elected to explore reflexive thematic analysis, advance and apply our analytic skills in applied qualitative health research, and provide direction and technique for researchers interested in this method of analysis. In this paper, we describe how a multidisciplinary graduate student group of applied health researchers utilized Braun and Clarke’s approach to reflexive thematic analysis. Specifically, we explore and describe our team’s process of data analysis used to analyze focus group data from a study exploring postnatal care referral behavior by traditional birth attendants in Nigeria. This paper illustrates our experience in applying the six phases of reflexive thematic analysis as described by Braun and Clarke: (1) familiarizing oneself with the data, (2) generating codes, (3) constructing themes, (4) reviewing potential themes, (5) defining and naming themes, and (6) producing the report. We highlight our experiences through each phase, outline strategies to support analytic quality, and share practical activities to guide the use of reflexive thematic analysis within an applied health research context and when working within research teams.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.230 | 0.355 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier 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".