A cross-sectional online evaluation of burnout risk factors among general surgical residents in Canada
Bibliographic record
Abstract
Background: Burnout is hallmarked by physical and psychological exhaustion, coupled with cynicism and disengagement. Evidence suggests that affected physicians not only suffer personally, but that patient safety and clinical outcomes are also negatively affected. This study aimed to identify potentially remediable risks for burnout among residents enrolled in Canadian general surgery programs.Methods: In this cross-sectional design, a questionnaire was distributed to every general surgery resident in the 15 programs consenting to participate. Questions examined the following five domains: demographics, working patterns, attitudes toward residency, life experiences, and lifestyle/outlook. Respondents' risks of burnout were assessed using the Maslach Burnout Inventory™. Univariate analysis and then multiple logistic regression were used to assess predictors.Results: A total of 114 completed questionnaires were received (22%). Of these residents, 39 (34%) met the criteria for high burnout risk. Inadequate personal/family time, a personal history of mental health or substance abuse-related issues, and moderately to poorly approachable staff/senior residents were all significantly associated with a high burnout risk (odds ratio [OR] =4.3, 95% confidence interval [CI] =1.6, 11.2, P = 0.003; OR =6.0, 95% CI = 1.6, 21.9, P = 0.007; and OR = 4.6, 95% CI = 1.7, 12.5, P = 0.003 respectively). Predicted high burnout risk probability with none of the above factors was 10%, increasing up to 40%, 75%, and 93% with one, two, or all of these risk factors present respectively.Conclusion: One-third of general surgery residents in Canada are at high burnout risk. Residency programs may have considerable influence over factors associated with this outcome to the benefit of residents, staff, and patients.The following core competencies are addressed in this article: Practice-based learning and improvement, Professionalism, Systems-based practice.
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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 | 0.001 | 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) | 0.001 | 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".