Insights From a National Survey on Gaps and Opportunities for Curriculum Improvement in Breast Imaging Education in Canadian Radiology Residency Programs
Bibliographic record
Abstract
Objective: Radiology residents must demonstrate competence in breast imaging prior to entering practice. Breast imaging fellowship training programs have attracted fewer trainees, contributing to the ongoing shortage in the field. This survey aimed to evaluate the structure, supports, and barriers to breast imaging training in Canadian residency programs to help guide future curriculum development. Methods: Following ethical approval, a 45-question cross-sectional survey was distributed to all 16 diagnostic radiology program directors of Canadian residency programs via email using the online platform SurveyMonkey between March and May 2024. The questions covered program demographics, breast imaging and procedural experience, integration into multidisciplinary care, the impact of other learners, scheduling, and departmental leadership. Results/Discussion: Twelve (75%) program directors across 7 provinces responded. Responses revealed variability in program structures and resources. All programs provided the suggested 4 months of breast imaging training. Exposure to interpreting screening mammography was offered by 75% (9/12) of programs, however, 75% (9/12) of programs did not involve residents in multidisciplinary rounds, limiting collaborative care training. Simulation sessions for breast imaging procedures were offered in 33% (4/12) of programs. Assessment methods across programs lacked uniformity, with only 17% (2/12) of programs using post-rotation tests, and all relying on subjective evaluations. Conclusion: This study highlights the need to address disparities and enhance standardization to improve residents’ breast imaging education. Establishing clear guidelines for integration of multimodality breast imaging exposure, increasing procedural training, and providing opportunities to participate in multidisciplinary care conferences are essential for developing a more uniform and comprehensive breast imaging curriculum nationally.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".