A practical guide to reflexivity in qualitative research: AMEE Guide No. 149
Bibliographic record
Abstract
Qualitative research relies on nuanced judgements that require researcher reflexivity, yet reflexivity is often addressed superficially or overlooked completely during the research process. In this AMEE Guide, we define reflexivity as a set of continuous, collaborative, and multifaceted practices through which researchers self-consciously critique, appraise, and evaluate how their subjectivity and context influence the research processes. We frame reflexivity as a way to embrace and value researchers' subjectivity. We also describe the purposes that reflexivity can have depending on different paradigmatic choices. We then address how researchers can account for the significance of the intertwined personal, interpersonal, methodological, and contextual factors that bring research into being and offer specific strategies for communicating reflexivity in research dissemination. With the growth of qualitative research in health professions education, it is essential that qualitative researchers carefully consider their paradigmatic stance and use reflexive practices to align their decisions at all stages of their research. We hope this Guide will illuminate such a path, demonstrating how reflexivity can be used to develop and communicate rigorous qualitative research.
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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 | 0.115 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.050 | 0.033 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".