Interpretive Autoethnography in Medicine: An Accessible Way to Introduce Healthcare Professionals to the Craft of Critical Qualitative Writing
Bibliographic record
Abstract
Qualitative research is valuable in medicine because of the deep insights it offers into the social and cultural dimensions of healthcare. Historically, qualitative methods have been influenced by critical theory and have shared its constructivist epistemology and orientation towards social justice. It can be challenging to teach such critical qualitative inquiry to healthcare professionals because its underlying philosophy can seem at odds with the objectivist biological perspective emphasized in medical education. This is unfortunate because several social inequities are perpetuated by modern healthcare systems and critical qualitative inquiry is essential to the project of addressing them. This article argues that Norman Denzin’s interpretive autoethnography is a promising method through which educators could introduce healthcare professionals to critical qualitative inquiry. In this method, the author uses the craft of writing creatively about their personal experiences as a tool for cultural interpretation and social justice activism. Such a creative analytic practice might seem alien to many medical professionals. On the other hand, the idea of analyzing their own experiences in detail is likely to feel familiar to them because of the prominence of reflective writing in healthcare professional development practice. This familiarity might make interpretive autoethnography accessible to healthcare professionals and practicing the method could help them to appreciate the value of interpretive writing as a way of investigating sociocultural meaning and promoting just change.
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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 | 0.068 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".