Dermatology postgraduate training in Canada: CanMEDS competencies
Bibliographic record
Abstract
Medical residency education and the development of formalized training objectives in Canada have evolved continuously, especially with the introduction of the Canadian Medical Education Directions for Specialists (CanMEDS) competencies by the Royal College of Physicians and Surgeons of Canada (RCPSC) in 1996. In order to evaluate the effectiveness of implementation of CanMEDS competencies in Canadian postgraduate dermatology training programs from the residents' perspective, a comprehensive national survey of all Canadian core dermatology residents was conducted in June 2004. One hundred percent of core (PGY3-5) dermatology residents across the country (n = 48) completed the survey. Forty eight percent of residents were familiar with the CanMEDS competencies. Within the CanMEDS framework, the competencies were felt to be taught adequately by the following proportion of residents: medical expert (78 %), professional (66 %), communicator (52 %), collaborator (48 %), health advocate (48 %), scholar (48 %), and manager (28 %), with notable differences based on the year of training. This is the first national Canadian survey examining dermatology postgraduate education from the residents' perspective with a focus on CanMEDS competencies. While the RCPSC CanMEDS project implementation is presently in the faculty development phase, further work must be accomplished to enhance awareness of CanMEDS competencies and to incorporate these into dermatology residency programs across the country. Particular targeting of the roles perceived to be poorly taught is needed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".