Black Ice: ways to get a grip on resident co-production within medical education change
Bibliographic record
Abstract
The Royal College of Physicians and Surgeons of Canada (RCPSC) is transforming its national approach to postgraduate medical education by transitioning all specialty programs to competency based medical education (CBME) curriculums over a seven-year period. Queen's University, with special permission from the RCPSC, launched CBME curricula for all incoming residents across its 29 specialty programs in July 2017. Resident engagement, empowerment, and co-production through this transition has been instrumental in successful implementation of CBME at Queen's University. This article aims to use our own experience at Queen's in the context of current literature and rooted in change leadership theory, to provide a guide for educators, learners, and institutions on how to leverage the interest and enthusiasm of trainees in the transition to CBME in postgraduate training. The following ten tips provides a model for avoiding the "black ice" type pitfalls that can arise with learner involvement and ensure a smoother transition for other institutions moving forward with 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 | 0.051 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.034 | 0.047 |
| Scholarly communication | 0.034 | 0.054 |
| Open science | 0.005 | 0.045 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".