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.
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 | 0.010 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".