Autoethnography: introducing ‘I’ into medical education research
Bibliographic record
Abstract
CONTEXT: Autoethnography is a methodology that allows clinician-educators to research their own cultures, sharing insights about their own teaching and learning journeys in ways that will resonate with others. There are few examples of autoethnographic research in medical education, and many areas would benefit from this methodology to help improve understanding of, for example, teacher-learner interactions, transitions and interprofessional development. OBJECTIVES: We wish to share this methodology so that others may consider it in their own education environments as a viable qualitative research approach to gain new insights and understandings. METHODS: This paper introduces autoethnography, discusses important considerations in terms of data collection and analysis, explores ethical aspects of writing about others and considers the benefits and limitations of conducting research that includes self. RESULTS: Autoethnography allows medical educators to increasingly engage in self-reflective narration while analysing their own cultural biographies. It moves beyond simple autobiography through the inclusion of other voices and the analytical examination of the relationships between self and others. Autoethnography has achieved its goal if it results in new insights and improvements in personal teaching practices, and if it promotes broader reflection amongst readers about their own teaching and learning environments. CONCLUSIONS: Researchers should consider autoethnography as an important methodology to help advance our understanding of the culture and practices of medical education.
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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 | 0.085 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".