Redesigning family medicine residency in Canada: the triple C curriculum.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite a record of excellence, Canadian family medicine residency programs must respond to the changing face of health care and the needs of the population. A working group was established by the College of Family Physicians of Canada to review the current curriculum and make recommendations for change. METHODS: Literature reviews of current evidence regarding strategies in postgraduate medical education were carried out, and recent developments in medical education internationally were studied. After recommendations for curriculum change were drafted, workshops, presentations, and peer consultations were conducted over a 4-year period to test ideas and obtain stakeholder feedback. RESULTS: The core recommendation of the working group is: Residency programs in family medicine are to establish a competency-based curriculum that is comprehensive, focused on continuity, and centered in family medicine--The Triple C Competency-based Curriculum. The working group developed a new framework for family medicine competency in Canada, CanMEDS-FM, to support the transition. CONCLUSIONS: The Triple C Competency-based Curriculum was developed to redesign Canadian family medicine residencies based on a solid rationale. Recommendations for curricular change, as well as the competency framework, CanMEDS-FM, have been accepted enthusiastically by stakeholders. Implementation and evaluation phases are 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".