Burnout in Canadian Radiology Residency: A National Assessment of Prevalence and Underlying Contributory Factors
Bibliographic record
Abstract
Objective: To determine burnout prevalence in Canadian radiology residency and identify contributing factors. Materials and Methods: A prospective 57-item survey, including the 22-item Maslach Burnout Inventory-Health Sciences Survey, was sent to all Canadian radiology residents, with a total resident population of 359. The association between emotional exhaustion (EE), depersonalization (DP), and personal achievement (PA) scores with items in the survey was performed. Continuous data were evaluated using the Student t test for comparing the means between the 2 groups or the analysis of variance test for comparing the means between at least 3 groups. Spearman correlation coefficient was performed when evaluating ordinal categorical data. Results: Response rate is 40.1% (n = 144); 50.7% of residents demonstrate high EE, 48.6% demonstrate high DP, and 35.9% demonstrate low PA. Being unhappy with residency and with radiology as a career is associated with burnout ( P < .001). Age, sex, marital status, and children have no impact on burnout. More hours worked is associated with higher EE ( P = .025) and DP ( P = .004). In all, 47.2% residents experienced intimidation or harassment. Feeling unsupported by staff radiologists is associated with higher EE ( P < .001), higher DP ( P = .001), and lower PA ( P = .008). In all, 45.1% of residents have poor work–life balance, and those residents demonstrate higher EE ( P < .001), higher DP ( P = .006), and lower PA ( P = .01). In all, 25% of residents identify poor education-service balance in their residency, and those residents have higher EE ( P < .001), higher DP ( P = .042), and lower PA ( P = .005). Conclusion: This study demonstrates significant burnout in Canadian radiology residents with major contributory factors identified.
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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 | 0.002 | 0.003 |
| 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) | 0.001 | 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".