Challenges to Learning Evidence-Based Medicine and Educational Approaches to Meet These Challenges
Bibliographic record
Abstract
PURPOSE: Evidence-based medicine (EBM) is a fixture in many medical school curricula. Yet, little is known about the challenges medical students face in learning EBM or the educational approaches that medical schools use to overcome these challenges. METHOD: A qualitative multi-institutional case study was conducted between December 2013 and July 2014. On the basis of the Association of American Medical Colleges 2012 Medical School Graduation Questionnaire data, the authors selected 22 U.S. and Canadian Liaison Committee on Medical Education-accredited medical schools with graduates reporting confidence in their EBM skills. Participants were interviewed and asked to submit EBM curricular materials. Interviews were audio-recorded, transcribed, and analyzed using an inductive approach. RESULTS: Thirty-one EBM instructors (17 clinicians, 11 librarians, 2 educationalists, and 1 epidemiologist) were interviewed from 17 medical schools (13 in the United States, 4 in Canada). Four common EBM learning challenges were identified: suboptimal role models, students' lack of willingness to admit uncertainty, a lack of clinical context, and students' difficulty mastering EBM skills. Five educational approaches to these challenges that were common across the participating institutions were identified: integrating EBM with other courses and content, incorporating clinical content into EBM training, EBM faculty development, EBM whole-task exercises, and longitudinal integration of EBM. CONCLUSIONS: The identification of these four learner-centered EBM challenges expands on the literature on challenges in teaching and practicing EBM, and the identification of these five educational approaches provides medical educators with potential strategies to inform the design of EBM curricula.
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How this classification was reachedexpand
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".