The CanMEDS scholar: the neglected competency in tomorrow's doctors
Bibliographic record
Abstract
Rele Ologunde,1 Ivana Di Salvo,2,3 Ankur Khajuria1 1Imperial College School of Medicine, Imperial College London, London, UK; 2Faculty of Medicine and Surgery, University of Pavia, Pavia, Italy; 3International Federation of Medical Students' Associations, Ferney-Voltaire, FranceIn 1996, the Royal College of Physicians and Surgeons of Canada proposed a competency-based framework describing the core competencies of specialist physicians, one of which was a scholar.1 The UK General Medical Council has since provided advice on developing teachers and trainers in undergraduate medical education.2 However, guidance about how to most effectively incorporate this advice into the medical curriculum remains unclear.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".