Ethnography in health professions education: Slowing down and thinking deeply
Bibliographic record
Abstract
CONTEXT: Ethnography has been gaining appreciation in the field of health professions education (HPE) research, yet it remains misunderstood. Our article contributes to this growing literature by describing some of the key tensions with which both aspiring and seasoned ethnographers should productively struggle. METHODS: We respond to the injunction made by Varpio et al (2017) that HPE researchers should ground their methodological ventures in their historical and philosophical tenets. To do so, we first review core ethnographic texts that provide a background for ethnographic research in HPE, then provide an orienting definition to bind the specificities of ethnographic research. Finally, we review core theoretical and practical considerations for ethnographic research. RESULTS: Ethnography is a slow and deep approach to knowledge production, and as such it requires careful engagement with theory and deliberate choice of methods. Core theoretical tensions include the ontological, epistemological and axiological dimensions of ethnography, and concerns with quality and rigour. Practical tensions include the scope and remit of ethnography, the importance of observing naturally occurring behaviour and the crafting of rich field notes. CONCLUSIONS: We encourage ethnographers to pursue scholarship that challenges the status quo. Ethnographers should favour deep encounters with research participants, dig deep into the cultural and structural aspects of HPE and be reflexive about knowledge outputs. At a time in HPE when the pressures to publish are high, using ethnography as a research methodology offers an opportunity to slow down and think deeply.
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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 | 0.210 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.014 | 0.109 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".