Implementation of a new undergraduate radiology curriculum: experience at the University of British Columbia.
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to review and revise the undergraduate radiology curriculum at the University of British Columbia to improve radiology education to medical students and to meet the needs of a medical program with province-wide distribution. METHODS: We identified the radiology content of the curriculum from the Curriculum Management and Information Tool online database, from personal interviews with curriculum heads, and from published information. Undergraduates' and recent graduates' opinions were solicited by means of surveys. Information on radiology curricula at medical schools across Canada was gathered from email surveys and personal contacts with members of the Canadian Heads of Academic Radiology (CHAR). RESULTS: Review of our curriculum indicated that lack of a unified syllabus resulted in redundant content, gaps in knowledge, and lack of continuity in the curriculum. Results from the survey of programs across Canada indicated that most schools also lacked a formal radiology curriculum for medical students. By adapting the guidelines from the Association of Medical Student Education in Radiology, we revised our undergraduate radiology curriculum to emphasize integration and self-learning. The modified curriculum includes a combination of instructional technology, focused lectures in preclinical years, and in-context seminars in clerkship rotations. CONCLUSION: Most medical schools in Canada do not have a formal radiology curriculum for medical students. A structured curriculum is required to improve the quality of radiology teaching for medical students.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".