Competency-based education in family medicine
Bibliographic record
Abstract
BACKGROUND: As a way of demonstrating an objective assessment of trainee competence, the College of Family Physicians of Canada has recently approved a competency-based framework known as CanMEDS-FM. All training programs in family medicine in Canada will be required to demonstrate the development of curriculum and evaluation methods based on the roles defined by the framework. AIM: This article describes the rationale and the approach used to develop a competency-based education curriculum in the postgraduate family medicine program at the University of Toronto. METHOD: The authors describe a systematic approach to curriculum development which includes the formation of a central steering committee, content development by faculty experts, mapping of curriculum to an accreditation framework, and a faculty consensus exercise. We discuss challenges to development and implementation of a competency-based framework as well as areas that require further work and development. CONCLUSIONS: The competency-based curriculum is both a new method of learning for residents and, a new method of teaching for faculty. While there are many potential benefits and challenges, this article focuses on the model's utility in terms of flexible learner-centered educational design, as well as its ability to identify learners' strengths and needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".