Etiology of Burnout in Canadian Radiologists and Trainees
Bibliographic record
Abstract
Purpose: There is worsening of burnout symptoms experienced by radiologists and trainees. We explored potential factors that exacerbate burnout symptoms observed in the Canadian radiological community and currently available protective factors as next steps for establishing viable solutions for burnout. Methods: An 11-question electronic survey was distributed to Canadian radiologists and trainees through the Canadian Association of Radiologists (CAR). Approval from a local ethics board and the CAR were obtained. The survey contained demographics-related questions as well as questions based on common risk factors for burnout. Qualitative and quantitative analyses were performed. Results: The survey was distributed to 2200 CAR members, and a response rate of 23.3% was achieved. Most radiologists experienced frequent unexpected high workload with no statistically significant difference by the type of practice. Trainees experienced a statistically significantly ( P < .0001) higher frequency of on-call shifts compared to staff radiologists. A statistically significant difference ( P < .0001) was observed for perceived threats to career longevity dependent on length of career. Although support mechanisms for radiology were perceived as available, survey commentary suggested inefficiency in their usage and lack of prioritization, which was a trend observed across all types of practice. Conclusions: While there is awareness for radiology needs, changes are required at the workplace level to reduce burnout symptoms at their source. Communication between radiologists and hospital administration, as well as among radiology group members, is key to prioritize radiology needs in our imaging-driven era of health care.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".