A survey study of evidence-based medicine training in US and Canadian medical schools
Bibliographic record
Abstract
PURPOSE: The authors conducted a survey examining (1) the current state of evidence-based medicine (EBM) curricula in US and Canadian medical schools and corresponding learning objectives, (2) medical educators' and librarians' participation in EBM training, and (3) barriers to EBM training. METHODS: A survey instrument with thirty-four closed and open-ended questions was sent to curricular deans at US and Canadian medical schools. The survey sought information on enrollment and class size; EBM learning objectives, curricular activities, and assessment approaches by year of training; EBM faculty; EBM tools; barriers to implementing EBM curricula and possible ways to overcome them; and innovative approaches to EBM education. Both qualitative and quantitative methods were used for data analysis. Measurable learning objectives were categorized using Bloom's taxonomy. RESULTS: One hundred fifteen medical schools (77.2%) responded. Over half (53%) of the 900 reported learning objectives were measurable. Knowledge application was the predominant category from Bloom's categories. Most schools integrated EBM into other curricular activities; activities and formal assessment decreased significantly with advanced training. EBM faculty consisted primarily of clinicians, followed by basic scientists and librarians. Various EBM tools were used, with PubMed and the Cochrane database most frequently cited. Lack of time in curricula was rated the most significant barrier. National agreement on required EBM competencies was an extremely helpful factor. Few schools shared innovative approaches. CONCLUSIONS: Schools need help in overcoming barriers related to EBM curriculum development, implementation, and assessment. IMPLICATIONS: Findings can provide a starting point for discussion to develop a standardized competency framework.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".