Knowledge and Perceptions of Competency-Based Medical Education in Diagnostic Radiology Post-Graduate Medical Education: Identifying Priorities and Developing a Framework for Professional Development Activities
Bibliographic record
Abstract
<b>Introduction:</b> We evaluated knowledge and perceptions of an established Competency-Based Medical Education (CBME) model developed by the Royal College of Physicians and Surgeons of Canada, Competence by Design (CBD), and identified evidence-informed priorities for professional development activities (PDAs). <b>Materials and Methods:</b> Teaching faculty and residents at a single, large diagnostic radiology post-graduate medical education (PGME) program were eligible to participate in this cross-sectional, survey-based study. Knowledge of CBD was evaluated through multiple choice questions (MCQs), which assessed participants’ understanding of major principles and terms associated with CBD. Participants’ perceptions of the anticipated impact of CBD on resident education and patient care were evaluated and priorities for PDAs were identified, which informed a framework for CBD PDAs. <b>Results:</b> Fifty faculty and residents participated. The faculty and resident response rates were 11.6% (n = 29/249) and 55.3% (n = 21/38), respectively. The mean ± standard deviation overall score on MCQs was 39.0% ± 20.4%. The majority of participants perceived the impact of CBD on resident education to be equivocal and to not impact patient care. Knowledge of CBD was not statistically significantly associated with participants’ perceptions of the impact of CBD on either resident education or patient care (<i>P</i> > .05). Delivery of high-quality feedback was the greatest priority identified for PDAs. <b>Discussion: </b>Our results and proposed CBD PDAs framework may help to guide diagnostic radiology PGME programs in designing evidence-informed PDAs, which may meaningfully contribute to the successful implementation of CBD in diagnostic radiology PGME. As diagnostic radiology PGME programs throughout the world increasingly implement CBME models, evidence-informed PDAs will become of increasing importance.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".