Implementing a competency based medical education curriculum in diagnostic radiology: Challenges and Pearls of Wisdom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it