Are some of the challenging aspects of the CanMEDS roles valid outside Canada?
Bibliographic record
Abstract
CONTEXT: Many countries have adopted the CanMEDS roles. However, there is limited information on how these apply in an international context and in different specialties. OBJECTIVES: To survey trainee and specialist ratings of the importance of the CanMEDS roles and perceived ability to perform tasks within the roles. METHODS: We surveyed 8749 doctors within a defined region (eastern Denmark) via a single-issue, mailed questionnaire. Each of the 7 roles was represented by 3 questionnaire items to be rated for perceived importance and confidence in ability to perform the role. RESULTS: Responses were received from 3476 doctors (42.8%), including 190 interns, 201 doctors in the introductory year of specialist training, 529 residents and 2152 specialists. The overall mean rating of importance (on a scale of 1-5) of the aspects of competence described in the CanMEDS roles was 4.2 (standard deviation 0.6) and did not differ between trainee groups and specialists. Mean ratings of confidence were lower than ratings of importance and increased across the groups from interns to specialists. Differences between specialty groups were evident in both importance and confidence for many of the roles. For laboratory, technical and, to a lesser extent, cognitive specialties, the role of Health Advocate scored the lowest in importance. For general medicine specialties, the roles of Medical Expert, Collaborator, Manager and Scholar all scored lower for importance and confidence. CONCLUSIONS: This study provides a sketch of the content and construct validity of the CanMEDS roles in a non-Canadian setting. More research is needed in how these aspects of competence can be best taught and applied across specialties in different jurisdictions.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".