Suicide and Self-Harm Among Physicians in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Studies of occupation-associated suicide suggest physicians may be at a higher risk of suicide compared to nonphysicians. We set out to assess the risk of suicide and self-harm among physicians and compare it to nonphysicians. METHODS: We conducted a population-based, retrospective cohort study using registration data from the College of Physicians and Surgeons of Ontario from 1990 to 2016 with a follow-up to 2017, linked to Ontario health administrative databases. Using age- and sex-standardized rates and inverse probability-weighted, cause-specific hazards regression models, we compared rates of suicide, self-harm, and a composite of either event among all newly registered physicians to nonphysician controls. RESULTS: Among 35,989 physicians and 6,585,197 nonphysicians, unadjusted suicide events (0.07% vs. 0.11%) and rates (9.44 vs. 11.55 per 100,000 person-years) were similar. Weighted analyses found a hazard ratio of 1.05 (95% confidence interval: 0.69 to 1.60). Self-harm requiring health care was lower among physicians (0.22% vs. 0.46%; hazard ratio: 0.65, 95% confidence interval: 0.52 to 0.82), as was the composite of suicide or self-harm (hazard ratio: 0.70, 95% confidence interval: 0.57 to 0.86). The composite of suicide or self-harm was associated with a history of a mood or anxiety disorder (odds ratio: 2.84, 95% confidence interval: 1.17 to 6.87), an outpatient mental health visit in the past year (odds ratio: 3.08, 95% confidence interval: 1.34 to 7.10) and psychiatry visit in the preceding year (odds ratio: 3.87, 95% confidence interval: 1.67 to 8.95). INTERPRETATION: Physicians in Ontario are at a similar risk of suicide deaths and a lower risk of self-harm requiring health care relative to nonphysicians. Risk factors associated with suicide or self-harm may help inform prevention programs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".