Utilization of evidence-based tools and medical education literature by Canadian postgraduate program directors in the teaching and assessment of the CanMEDS roles
Bibliographic record
Abstract
BACKGROUND: Researchers have shown that clinical educators feel insufficiently informed about how to teach and assess the CanMEDS roles. Thus, our objective was to examine the extent to which program directors utilize evidence-based tools and the medical education literature in teaching and assessing the CanMEDS roles. METHODS: In 2016, the authors utilized an online questionnaire to survey 747 Canadian residency program directors (PD's) of Royal College of Physicians and Surgeons of Canada (RCPSC) accredited programs. RESULTS: Overall, 186 PD's participated (24.9%). 36.6% did not know whether the teaching strategies they used were evidence-based and another third (31.9%) believed they were "not at all" or "to a small extent" evidence-based. Similarly, 31.8% did not know whether the assessment tools they used were evidence-based and another third (39.7%) believed they were "not at all" or "to a small extent" evidence-based. PD's were aware of research on teaching strategies (62.4%) and assessment tools (51.9%), but felt they did not have sufficient time to review relevant literature (72.1% for teaching and 64.1% for assessment). CONCLUSIONS: Canadian PD's reported low awareness of evidence-based tools for teaching and assessment, implying a potential knowledge translation gap in medical education research.
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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 | 0.003 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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".