Creating a Competency-Based Medical Education Curriculum for Canadian Diagnostic Radiology Residency (Queen’s Fundamental Innovations in Residency Education)-Part 2: Core of Discipline Stage
Bibliographic record
Abstract
PURPOSE: All postgraduate residency programs in Canada are transitioning to a competency-based medical education (CBME) model divided into 4 stages of training. Queen's University has been the first Canadian institution to mandate transitioning to CBME across all residency programs, including Diagnostic Radiology. This study describes the implementation of CBME with a focus on the third developmental stage, Core of Discipline, in the Diagnostic Radiology residency program at Queen's University. We describe strategies applied and challenges encountered during the adoption and implementation process in order to inform the development of other CBME residency programs in Diagnostic Radiology. METHODS: At Queen's University, the Core of Discipline stage was developed using the Royal College of Physicians and Surgeons of Canada's (RCPSC) competence continuum guidelines and the CanMEDS framework to create radiology-specific entrustable professional activities (EPAs) and milestones for assessment. New committees, administrative positions, and assessment strategies were created to develop these assessment guidelines. Currently, 2 cohorts of residents (n = 6) are enrolled in the Core of Discipline stage. RESULTS: EPAs, milestones, and methods of evaluation for the Core of Discipline stage are described. Opportunities during implementation included tracking progress toward educational objectives and increased mentorship. Challenges included difficulty meeting procedural volume requirements, inconsistent procedural tracking, improving feedback mechanisms, and administrative burden. CONCLUSION: The transition to a competency-based curriculum in an academic Diagnostic Radiology residency program is significantly resource and time intensive. This report describes challenges faced in developing the Core of Discipline stage and potential solutions to facilitate this process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".