Creating a Competency-Based Medical Education Curriculum for Canadian Diagnostic Radiology Residency (Queen’s Fundamental Innovations in Residency Education)—Part 1: Transition to Discipline and Foundation of Discipline Stages
Bibliographic record
Abstract
PURPOSE: The Royal College of Physicians and Surgeons of Canada (RCPSC) has mandated the transition of postgraduate medical training in Canada to a competency-based medical education (CBME) model divided into 4 stages of training. As part of the Queen's University Fundamental Innovations in Residency Education proposal, Queen's University in Canada is the first institution to transition all of its residency programs simultaneously to this model, including Diagnostic Radiology. The objective of this report is to describe the Queen's Diagnostic Radiology Residency Program's implementation of a CBME curriculum. METHODS: At Queen's University, the novel curriculum was developed using the RCPSC's competency continuum and the CanMEDS framework to create radiology-specific entrustable professional activities (EPAs) and milestones. In addition, new committees and assessment strategies were established. As of July 2015, 3 cohorts of residents (n = 9) have been enrolled in this new curriculum. RESULTS: EPAs, milestones, and methods of evaluation for the Transition to Discipline and Foundations of Discipline stages, as well as the opportunities and challenges associated with the implementation of a competency-based curriculum in a Diagnostic Radiology Residency Program, are described. Challenges include the increased frequency of resident assessments, establishing stage-specific learner expectations, and the creation of volumetric guidelines for case reporting and procedures. CONCLUSIONS: Development of a novel CBME curriculum requires significant resources and dedicated administrative time within an academic Radiology department. This article highlights challenges and provides guidance for this process.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".