The Impact of a National Competency-Based Medical Education Initiative in Family Medicine
Bibliographic record
Abstract
PURPOSE: Triple C is the Canadian competency-based medical education (CBME) initiative for family medicine. The authors report on a study exploring the impacts Triple C has had across Canada. METHOD: A multi-institutional team conducted a realist study to explore the impact of Triple C implementation in different programs across Canada. Data were collected between March and June 2016 from interviews and focus groups with key medical school stakeholders. Data were analyzed using thematic and template analysis techniques. RESULTS: Data were acquired from 16 of the 17 Canadian medical schools from a combination of program leaders, educators, and trainees. Triple C was implemented in different ways and to different extents depending on context. Newer sites tended to have a more comprehensive implementation than established sites. Urban sites afforded different opportunities to implement Triple C from those afforded by rural sites. Although it was too early to assess its impact on the quality of graduating residents, Triple C was seen as having had a positive impact on identifying and remediating failing learners and on energizing and legitimizing the educational mission in family medicine. Negative impacts included greater costs and tensions in the relationships with other specialties. A principles-based approach to CBME offered flexibility to programs to incorporate variation in their interpretation and implementation of Triple C. Although there was a degree of normalization of practice, it was not standardized across sites or programs. CONCLUSIONS: Triple C has been successfully implemented across Canada but in differing ways and with different impacts.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| 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".