Characteristics of evidence-based medicine training in Royal College of Physicians and Surgeons of Canada emergency medicine residencies - a national survey of program directors
Bibliographic record
Abstract
BACKGROUND: Recent surveys suggest few emergency medicine (EM) training programs have formal evidence-based medicine (EBM) or journal club curricula. Our primary objective was to describe the methods of EBM training in Royal College of Physicians and Surgeons of Canada (RCPSC) EM residencies. Secondary objectives were to explore attitudes regarding current educational practices including e-learning, investigate barriers to journal club and EBM education, and assess the desire for national collaboration. METHODS: A 16-question survey containing binary, open-ended, and 5-pt Likert scale questions was distributed to the 14 RCPSC-EM program directors. Proportions of respondents (%), median, and IQR are reported. RESULTS: The response rate was 93% (13/14). Most programs (85%) had established EBM curricula. Curricula content was delivered most frequently via journal club, with 62% of programs having 10 or more sessions annually. Less than half of journal clubs (46%) were led consistently by EBM experts. Four programs did not use a critical appraisal tool in their sessions (31%). Additional teaching formats included didactic and small group sessions, self-directed e-learning, EBM workshops, and library tutorials. 54% of programs operated educational websites with EBM resources. Program directors attributed highest importance to two core goals in EBM training curricula: critical appraisal of medical literature, and application of literature to patient care (85% rating 5 - "most importance", respectively). Podcasts, blogs, and online journal clubs were valued for EBM teaching roles including creating exposure to literature (4, IQR 1.5) and linking literature to clinical practice experience (4, IQR 1.5) (1-no merit, 5-strong merit). Five of thirteen respondents rated lack of expert leadership and trained faculty educators as potential limitations to EBM education. The majority of respondents supported the creation of a national unified EBM educational resource (4, IQR 1) (1-no support, 5- strongly support). CONCLUSIONS: RCPSC-EM programs have established EBM teaching curricula and deliver content most frequently via journal club. A lack of EBM expert educators may limit content delivery at certain sites. Program directors supported the nationalization of EBM educational resources. A growing usage of electronic resources may represent an avenue to link national EBM educational expertise, facilitating future collaborative educational efforts.
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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 | 0.010 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".