Implementing a competency based medical education curriculum in diagnostic radiology: Challenges and Pearls of Wisdom
Bibliographic record
Abstract
In 2014, The Royal College of Physicians and Surgeons of Canada (RCPSC) began a national initiative to rollout Competency-Based Medical Education (CBME) for all postgraduate medical programs. This represents a paradigm shift in the approach to resident training and transformative changes on many levels. In 2017, the department of Diagnostic Radiology at Queen's University became an early adopter of the CBME training model. The department began curricular planning using program specific Entrustable Professional Activities (EPAs), milestones based on the CanMeds roles, and an assessment framework. Associated processes were created to support implementation, with formation of a new competence committee, structure and process for academic advisors, and faculty development. In July 2018, the model was implemented using an electronic portfolio system, Elentra. In July 2022, the RCPSC launched the national implementation of their CBME CBD model, which was implemented for the incoming cohorts in the department of Diagnostic Radiology. Drawing from CBME implementation in the department of Diagnostic Radiology at Queen's University, we highlight the challenges encountered at our institution, methods for addressing these challenges, and corresponding outcomes. From our experience, we aim to provide a roadmap for the reader that will aid in planning for CBME implementation at other institutions.
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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 | 0.083 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".