Implementing Competency-Based Medical Education in a Postgraduate Family Medicine Residency Training Program: A Stepwise Approach, Facilitating Factors, and Processes or Steps That Would Have Been Helpful
Bibliographic record
Abstract
PROBLEM: In 2009-2010, the postgraduate residency training program at the Department of Family Medicine, Queen's University, wrestled with the practicalities of competency-based medical education (CBME) implementation when its accrediting body, the College of Family Physicians of Canada, introduced the competency-based Triple C curriculum. APPROACH: The authors used a stepwise approach to implement CMBE; the steps were to (1) identify objectives, (2) identify competencies, (3) map objectives and competencies to learning experiences and assessment processes, (4) plan learning experiences, (5) develop an assessment system, (6) collect and interpret data, (7) adjust individual residents' training programs, and (8) distribute decisions to stakeholders. The authors also note overarching processes, costs, and facil itating factors and processes or steps that would have been helpful for CBME implementation. OUTCOMES: Early outcomes are encouraging. Residents are being directly observed more often with increased documented feedback about performance based on explicit competency standards (24,000 data points for 150 residents from 2013 to 2015). These multiple observations are being collated in a way that is allowing the identification of patterns of performance, red flags, and competency development trajectory. Outliers are being identified earlier, resulting in earlier individualized modification of their residency training program. NEXT STEPS: The authors will continue to provide and refine faculty development, are developing an entrustable professional activity field note app for handheld devices, and are undertaking research to explore what facilitates learners' competency development, what increases assessors' confidence in making competence decisions, and whether residents are better trained as a result of CBME implementation.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".