Shared Canadian Curriculum in Family Medicine (SHARC-FM): Creating a national consensus on relevant and practical training for medical students.
Bibliographic record
Abstract
PROBLEM ADDRESSED: In 2006, leaders of undergraduate family medicine education programs faced a series of increasing curriculum mandates in the context of limited time and financial resources. Additionally, it became apparent that a hidden curriculum against family medicine as a career choice was active in medical schools. OBJECTIVE OF PROGRAM: The Shared Canadian Curriculum in Family Medicine was developed by the Canadian Undergraduate Family Medicine Education Directors and supported by the College of Family Physicians of Canada as a national collaborative project to support medical student training in family medicine clerkship. Its key objective is to enable education leaders to meet their educational mandates, while at the same time countering the hidden curriculum and providing a route to scholarship. PROGRAM DESCRIPTION: ). It contains 23 core clinical topics (determined through a modified Delphi process) with demonstrable objectives for each. It also includes low- and medium-fidelity virtual patient cases, point-of-care learning resources (clinical cards), and assessment tools, all aligned with the core topics. French translation of the resources is ongoing. CONCLUSION: The core topics, objectives, and educational resources have been adopted by medical schools across Canada, according to their needs. The lessons learned from mounting this multi-institutional collaborative project will help others develop their own collaborative curricula.
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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 | 0.056 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".