A retrospective cohort study comparing non-fatal self-harm emergency department visits between Canadian veterans living in Ontario and matched civilians
Bibliographic record
Abstract
This was a retrospective cohort study linking provincial administrative databases to compare rates of non-fatal self-harm between CAF and RCMP veterans living in Ontario and age-matched civilians. This study included male veterans who registered for provincial health insurance between 2002 and 2013. A civilian comparator group was matched 4:1 on age and sex. Self-harm emergency department (ED) visits were identified from provincial ED admission records until death or December 31, 2015. Multivariable Poisson regression compared the risk of self-harm. Analyses adjusted for age, geography, income, rurality, and major physical and mental comorbidities. In total, 9514 male veterans and 38,042 age- and sex-matched civilians were included. Overall, 0.55% of veterans had at least one non-fatal self-harm ED visit, compared with 0.81% of civilians. The rate of ED self-harm visits was 40% lower in the veteran population, compared to the civilian population (RR = 0.60; 95% CI = 0.41-0.87). In both groups, psychosocial and physical comorbidities, and death by suicide were more common in those who self-harmed than those who did not. A better understanding of why veterans have a lower rate of self-harm emergency department visits and how it is related to the number of completed suicides is an important area for future consideration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".