Ten tips for conducting focused ethnography in medical education research
Bibliographic record
Abstract
Background: Medical education researchers increasingly use qualitative methods, such as ethnography to understand shared practices and beliefs in groups. Focused ethnography (FE) is gaining popularity as a method that examines sub-cultures and familiar settings in a short time. However, the literature on how FE is conducted in medical education is limited.Aim: This paper provides 10 practical tips for conducting FE in medical education research.Methods: The tips were developed based on our expertise in ethnographic research and existing literature.Results: The 10 tips include: (1) Know the difference, (2) Build relationships before you start, (3) Have shared purpose and knowledge translation strategies with your stakeholders (4) Practice being reflexive, (5) Align research question with methodology, (6) Prepare your fieldwork, (7) Use a variety of methods for data collection, (8) Consider context on micro, meso, and macro levels, (9) Use triangulation, and (10) Provide a ‘thick description’,Conclusions: These 10 tips give practical guidance to medical educators in thinking about how and when to conduct FE.
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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 | 0.249 | 0.429 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".