Striving for excellence: developing a framework for the Triple C curriculum in family medicine education.
Bibliographic record
Abstract
PROBLEM ADDRESSED: Postgraduate medical education programs will need to be restructured in order to respond to curriculum initiatives promoted by the College of Family Physicians of Canada. OBJECTIVE OF PROGRAM: To develop a framework for the Triple C Competency-based Curriculum that will help provide residents with quality family medicine (FM) education programs. PROGRAM DESCRIPTION: The Family Medicine Curriculum Framework (FMCF) incorporates the 4 principles of FM, the CanMEDs-FM roles, the Triple C curriculum principles, the curriculum content domains, and the pedagogic strategies, all of which support the development of attitudes, knowledge, and skills in postgraduate FM training programs. CONCLUSION: The FMCF was an effective approach to the development of an FM curriculum because it incorporated not only core competencies of FM health education but also contextual educational values, principles, and dynamic learning approaches. In addition, the FMCF provided a foundation and quality standard to designing, delivering, and evaluating the FM curriculum to ensure it met the needs of FM education stakeholders, including preceptors, residents, and patients and their families.
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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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".