Relation between Traumatic Events and Suicide Attempts in Canadian Military Personnel
Bibliographic record
Abstract
OBJECTIVE: To determine whether exposure to particular types of traumatic events was differentially associated with suicide attempts in a representative sample of active military personnel. METHOD: Data came from the Canadian Community Health Survey: Mental Health and Well-Being Canadian Forces Supplement (CCHS-CFS), a cross-sectional survey that provided a comprehensive examination of mental disorders, health, and the well-being of currently active Canadian military personnel (n = 8441; aged 16 to 54 years; response rate 81.1%). Respondents were asked about exposure to 28 traumatic events that occurred during their lifetime. Suicide attempts were measured using a question about whether the person ever "attempted suicide or tried to take [his or her] own life." RESULTS: The prevalence of lifetime suicide attempts for currently active Canadian military men and women was 2.2% and 5.6%, respectively. Sexual and other interpersonal traumas (for example, rape, sexual assault, spousal abuse, child abuse) were significantly associated with suicide attempts in both men (adjusted odds ratios [AORs] ranging from 2.31 to 4.43) and women (AORs ranging from 1.73 to 3.71), even after adjusting for sociodemographics and mental disorders. Additionally, the number of traumatic events experienced was positively associated with increased risk of suicide attempts, indicating a dose-response effect of exposure to trauma. CONCLUSIONS: The current study is the first to demonstrate that sexual and other interpersonal traumatic events are associated with suicide attempts in a representative sample of active Canadian military men and women.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".