Ethnography in qualitative educational research: AMEE Guide No. 80
Bibliographic record
Abstract
Ethnography is a type of qualitative research that gathers observations, interviews and documentary data to produce detailed and comprehensive accounts of different social phenomena. The use of ethnographic research in medical education has produced a number of insightful accounts into its role, functions and difficulties in the preparation of medical students for clinical practice. This AMEE Guide offers an introduction to ethnography - its history, its differing forms, its role in medical education and its practical application. Specifically, the Guide initially outlines the main characteristics of ethnography: describing its origins, outlining its varying forms and discussing its use of theory. It also explores the role, contribution and limitations of ethnographic work undertaken in a medical education context. In addition, the Guide goes on to offer a range of ideas, methods, tools and techniques needed to undertake an ethnographic study. In doing so it discusses its conceptual, methodological, ethical and practice challenges (e.g. demands of recording the complexity of social action, the unpredictability of data collection activities). Finally, the Guide provides a series of final thoughts and ideas for future engagement with ethnography in medical education. This Guide is aimed for those interested in understanding ethnography to develop their evaluative skills when reading such work. It is also aimed at those interested in considering the use of ethnographic methods in their own research work.
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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 | 0.047 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".