Effectiveness of a 12-Month Emergency Radiology Curriculum for Improving Self-Confidence and Competence of Postgraduate Year 1 Radiology Residents—A Canadian Study
Bibliographic record
Abstract
Purpose: Limited radiology curriculum during postgraduate year 1 of radiology residency combined with increasing workloads during emergency radiology call have contributed to heightened anxiety and responsibilities for junior residents. This study aimed to evaluate the effectiveness of a 12-month emergency radiology curriculum on self-rated confidence and general competence of Canadian postgraduate year 1 radiology residents. Methods: A cohort of Canadian postgraduate year 1 Diagnostic Radiology residents voluntarily enrolled in a 12-month self-directed online emergency radiology curriculum (9 modules). Participants completed pretest and posttest surveys and examinations to gauge their self-rated competence on module material and knowledge acquisition, respectively. Average pretest and posttest scores were compared using Student 2-tailed unpaired t test, and Likert data from self-reported confidence were compared using a Mann Whitney U test. Statistical significance was defined as P < .05. Results: Sixty-six trainees completed at least 1 module, and 15 trainees completed all 9 modules. Both self-rated confidence and posttest scores were statistically higher after module completion ( P < .001) for all 9 learning modules. The greatest improvement in test scores was seen in the female genitourinary module (28.12 ± 3.018; difference between pretest and posttest means ± SEM). Conclusions: Our study demonstrates learning benefits for junior radiology trainees who participated in a self-directed online emergency radiology curriculum during postgraduate year 1. In the face of ever-increasing demands for imaging in on-call settings across Canada, inclusion of a self-directed online curriculum may become more important for upcoming competency-based medical education as it encourages a learner-driven and non-time-based method of education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".