Strategies to Enable Transformation in Medical Education: Faculty and Trainee Development in Competence By Design
Bibliographic record
Abstract
Transformative changes in health professions education need to incorporate effective faculty development, but few very large-scale faculty development designs have been described. The Royal College of Physicians and Surgeons of Canada's Competence by Design project was launched to transform the delivery of postgraduate medical education in Canada using a competency-based model. In this paper we outline the goals, principles, and rationale of the Royal College's national strategy for faculty and resident development initiatives to support the implementation of Competence by Design. We describe the activities and resources for both faculty and trainees that facilitated the redesign of training programs for each specialty and subspecialty at the national level, as well as supporting the implementation of the redesign at the local level. This undertaking was not without its challenges: we thus reflect on those challenges, enablers, and the lessons learned, and discuss a continuous quality improvement approach that was taken to iteratively inform the implementation process moving forward.
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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 | 0.172 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".