Prevalence of Burnout among Canadian Radiologists and Radiology Trainees
Bibliographic record
Abstract
PURPOSE: Physician burnout is on the rise compared to the average population, and radiology burnout rates are ranked high compared to other specialties. We aim to assess radiologist and radiology trainee burnout in Canada. METHODS: A survey using the abbreviated 7-item Maslach Burnout Inventory that characterizes burnout symptoms into personal accomplishment, emotional exhaustion, and depersonalization was sent to all eligible members of the Canadian Association of Radiologists in January 2018. The anonymous survey was hosted on SurveyMonkey for 1 month. A reminder e-mail was sent halfway through the survey period. RESULTS: Overall, 262 of 1401 invited radiology trainees and radiologists completed the survey (response rate 18.7%). With regards to personal accomplishment, we observed that (1) burnout in this domain improved with increased years worked and (2) milder symptoms were observed in community radiologists compared with their academic counterparts. In comparison with other studies of radiologist burnout, we found mild burnout symptoms in personal accomplishment, but severe symptoms in the burnout domains of both emotional exhaustion and depersonalization. CONCLUSIONS: Canadian radiologists and radiology trainees reported above average burnout symptoms with regard to both emotional exhaustion and depersonalization. Future research directions include exploring etiologies of burnout and implementation of treatment strategies based on these identified problem areas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".