A systems approach for institutional CBME adoption at Queen’s University
Bibliographic record
Abstract
The Royal College of Physicians and Surgeons of Canada (RCPSC) has begun the transition to Competency by Design (CBD), a new curricular model for residency education that 'ensure[s] competence, but teaches for excellence'. By 2022, all Canadian specialty programs are anticipated to have completed the CBD cohort process which includes workshops facilitated by a Royal College Clinician Educator. Queen's University in Ontario, Canada, was granted approval by the RCPSC to embark upon an accelerated path to competency-based medical education (CBME) for all our postgraduate specialties. This accelerated path allowed us to take an institutional approach for CBME implementation and ensure that all specialities were part of a system-wide change. Our unique institution-wide approach to CBD is the first of its kind across Canada. From both a theoretical and practical perspective we undertook CBME using a systems approach that allowed us to build the foundations for CBME, implement the change, and plan for sustainability. This has created opportunities to bridge and connect the various programs involved in the implementation of CBME on Queen's campus. The systems approach was an essential part of our strategy to develop a community dedicated to ensuring a successful 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 | 0.039 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".