Competency-Based Medical Education in Radiology: A Survey of Medical Student Perceptions
Bibliographic record
Abstract
PURPOSE: Implementing competency-based medical education in diagnostic radiology residencies will change the paradigm of learning and assessment for residents. The objective of this study is to evaluate medical student perceptions of competency-based medical education in diagnostic radiology programs and how this may affect their decision to pursue a career in diagnostic radiology. METHODS: First-, second-, and third-year medical students at a Canadian university were invited to complete a 14-question survey containing a mix of multiple choice, yes/no, Likert scale, and open-ended questions. This aimed to collect information on students' understanding and perceptions of competency-based medical education and how the transition to competency-based medical education would factor into their decision to enter a career in diagnostic radiology. RESULTS: The survey was distributed to 300 medical students and received 63 responses (21%). Thirty-seven percent of students had an interest in pursuing diagnostic radiology that ranged from interested to committed and 46% reported an understanding of competency-based medical education and its learning approach. The implementation of competency-based medical education in diagnostic radiology programs was reported to be a positive factor by 70% of students and almost all reported that breaking down residency into measurable milestones and required case exposure was beneficial. CONCLUSIONS: This study demonstrates that medical students perceive competency-based medical education to be a beneficial change to diagnostic radiology residency programs. The changes accompanying the transition to competency-based medical education were favored by students and factored into their residency decision-making.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".