How to teach evidence‐based medicine to teachers: reflections from a workshop experience
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: To summarize 20-year experience of conducting a workshop designed for educators who wish to improve their teaching skills of evidence based medicine (EBM). The goal is to provide tips for educators interested in replicating this educational model. METHODS: Qualitative description of factors associated with the success of the workshop. RESULTS: The factors considered by instructors to be most helpful are: the small group interactive design, role-play and simulation of real world learning environments, a mentorship model and high educator to learner ratio. CONCLUSIONS: Although this experience is observational and does not represent high quality evidence, certain attributes in the design of EBM workshops may lead to better dissemination of EBM concepts. Educators may consider empirically applying some of these attributes and testing their efficacy in comparative studies.
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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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".