Enabling Implementation of Competency Based Medical Education through an Outcomes-Focused Accreditation System
Bibliographic record
Abstract
Competency based medical education is being adopted around the world. Accreditation plays a vital role as an enabler in the adoption and implementation of competency based medical education, but little has been published about how the design of an accreditation system facilitates this transformation. The Canadian postgraduate medical education environment has recently transitioned to an outcomes-based accreditation system in parallel with the adoption of competency based medical education. Using the Canadian example, we characterize four features of an accreditation system that can facilitate the implementation of competency based medical education: theoretical underpinning, quality focus, accreditation standards, and accreditation processes. Alignment of the underlying educational theories within the accreditation system and educational paradigm drives change in a consistent and desired direction. An accreditation system that prioritizes quality improvement over quality assurance promotes educational system development and progressive change. Accreditation standards that achieve the difficult balance of being sufficiently detailed yet flexible foster a high fidelity of implementation without stifling innovation. Finally, accreditation processes that recognize the change process, encourage program development, and are not overly punitive all enable the implementation of competency based medical education. We also discuss the ways in which accreditation can simultaneously hinder the implementation of this approach. As education bodies adopt competency based medical education, particular attention should be paid to the role that accreditation plays in successful 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 | 0.062 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".