Which Aspects of the CanMEDS Competencies are Most Valued in Radiologists? Perspectives of Trainees From Other Specialties
Bibliographic record
Abstract
PURPOSE: Radiologists work primarily in collaboration with other healthcare professionals. As such, these stakeholder perspectives are of value to the development and assessment of educational outcomes during the transition to competency-based medical education. Our aim in this study was to determine which aspects of the Royal College CanMEDS competencies for diagnostic radiology are considered most important by future referring physicians. METHODS: Institutional ethics approval was obtained. After pilot testing, an anonymous online survey was sent to all residents and clinical fellows at our university. Open-ended questions asked respondents to describe the aspects of radiologist service they felt were most important. Thematic analysis of the free-text responses was performed using a grounded theory approach. The resulting themes were mapped to the 2015 CanMEDS Key Competencies. RESULTS: 115 completed surveys were received from residents and fellows from essentially all specialties and years of training (out of 928 invited). Major themes were 1) timeliness and accessibility of service, 2) quality of reporting, and 3) acting as a valued team member. The competencies identified as important by resident physicians were largely consistent with the CanMEDS framework, although not all key competencies were covered in the responses. CONCLUSIONS: This study illustrates how CanMEDS roles and competencies may be exemplified in a concrete and specialty-specific manner from the perspective of key stakeholders. Our survey results provide further insight into specific objectives for teaching and assessing these competencies in radiology residency training, with the ultimate goal of improving patient care through strengthened communication and working relationships.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".