Exploring Change After Implementation of Family Medicine Residency Curriculum Reform
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In 2010, the College of Family Physicians of Canada (CFPC) launched its competency-based medical education (CBME) approach to residency curriculum and assessment. Named Triple C, this innovation was developed to ensure graduates of family medicine training programs are competent to begin unsupervised practice. Further, Triple C was intended to promote interest in practicing comprehensive family medicine. A program evaluation plan was launched by the CFPC alongside the implementation of Triple C to explore if intended outcomes were achieved. METHODS: We conducted retrospective secondary data analysis of survey findings from graduating family medicine residents from two sources: National Physician Survey (NPS 2007 and 2010); and the Family Medicine Longitudinal Survey (FMLS 2015). Demographics and practice intentions reported by residents in the NPS 2007, NPS 2010, and FMLS 2015 were included in the analyses and a comparison between years was undertaken using a series of Pearson χ2 test. RESULTS: Findings indicate that in comparison to pre-Triple C (NPS 2007 and NPS 2010), significantly more residents reported the intention to include palliative care, intrapartum care, in-patient hospital care, care in the home, and practicing in rural settings after the implementation of Triple C (FMLS 2015; P<0.01). CONCLUSIONS: Family medicine graduates report an increase in intention to include a broader range of clinical domains after implementation of Triple C. While a causal relationship cannot be determined, using a historical control in the form of survey data that predates Triple C implementation could support future approaches to evaluation of education reform.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".