Delivering on the promise of competency based medical education – an institutional approach
Bibliographic record
Abstract
The Royal College of Physicians and Surgeons of Canada (RCPSC) adopted a plan to transform, over a seven-year horizon (2014-2021), residency education across all specialties to competency-based medical education (CBME) curriculum models. The RCPSC plan recommended implementing a more responsive and accountable training model with four discrete stages of training, explicit, specialty specific entrustable professional activities, with associated milestones, and a programmatic approach to assessment across residency education. Embracing this vision, the leadership at Queen's University (in Kingston, Ontario, Canada) applied for and was granted special permission by the RCPSC to embark on an accelerated institutional path. Over a three-year period, Queen's took CBME from concept to reality through the development and implementation of a comprehensive strategic plan. This perspective paper describes Queen's University's approach of creating a shared institutional vision, outlines the process of developing a centralized CBME executive team and twenty-nine CBME program teams, and summarizes proactive measures to ensure program readiness for launch. In so doing, Queen's created a community of support and CBME expertise that reinforces shared values including fostering co-production, cultivating responsive leadership, emphasizing diffusion of innovation, and adopting a systems-based approach to transformative change.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".