Introduction to Core Competencies in Residency: A Description of an Intensive, Integrated, Multispecialty Teaching Program
Bibliographic record
Abstract
Postgraduate residency programs must ensure that residents are properly trained in all core competencies. The CanMEDS framework of the Royal College of Physicians and Surgeons of Canada has established seven such competencies: medical expert, communicator, collaborator, manager, health advocate, scholar, and professional. The authors describe an integrated, one-month multispecialty rotation for first-year residents, Introduction to CanMEDS Core Competencies, at Laval University, Quebec, Canada. The goal of the rotation was to offer an in-depth and simultaneous training in each of the seven competencies. A pilot rotation was offered from February 9 to March 7, 2004 and involved 42 residents from seven programs and 30 faculty. It addressed 12 content areas related to the core competencies, through teaching formats promoting experiential and reflective learning. It involved three significant innovations: an intensive month-long format, during which residents were freed from most clinical duties; a multispecialty teaching and socialization strategy between peers and with faculty; and an integrated reflective approach, to ensure residents' understanding of the relevance and application of the core competencies in their own specialty. Although demanding to organize, the pilot rotation was well received. Residents were rapidly introduced to all competencies, and they developed an integrated perspective of them. An evaluation of impact is underway.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".