Ten tips for conducting focused ethnography in medical education research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 it