Burnout and Wellness Interventions Among Canadian Radiology Trainees: A Single Institution Study
Bibliographic record
Abstract
PURPOSE: This study examines factors contributing to burnout among radiology residents through a Canadian lens and assesses strategies employed at our institution to mitigate its impact. METHODS: This was a single-institution cross-sectional study. Four anonymous online surveys were administered through Qualtrics to PGY 2-5 radiology residents from 2021 to 2025. These surveys identified residents with burnout and distress and assessed contributing factors, suggestions for reducing burnout, and residents' responses to implemented interventions. Interventions were employed at 2 hospitals within our institution. RESULTS: = .167). Top factors driving burnout included time (eg, increased work hours, time constraints), extra duties (clinical and administrative), and perceived lack of radiology knowledge when dealing with complex cases. Interventions included additional daily 1-hour teaching sessions, wellness lunch rounds, debriefing sessions, transitioning from paper-based protocolling to a hybrid-electronic paper-based system, call schedule modifications, improved ergonomics, and social functions, including incorporating indoor and outdoor activities. Interventions targeting work hours were subjectively the most well-received in combating burnout. CONCLUSION: This study underscores the prevalence of burnout among radiology residents. Our institution has implemented a multi-faceted approach to address burnout within our radiology residency program.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".