A longitudinal examination of suicidal behaviours among individuals with mental disorders in the Canadian Armed Forces
Bibliographic record
Abstract
A high percentage of Canadian Armed Forces (CAF) members and veterans will be diagnosed with a mental disorder, and many also experience suicidal behaviours. This study examined demographic characteristics, potentially protective factors, and distal and proximal risk factors that may be related to suicidal behaviour (ideation, plans and attempts) over a 16-year period among CAF members and veterans who met criteria for a mental disorder at baseline. This study utilized data from the 2018 CAF Members and Veterans Mental Health Follow-up Survey (n = 2,941) with respondents from the 2002 Canadian Community Health Survey: Canadian Forces Supplement. Logistic regression analyses were conducted using subsamples with a lifetime diagnosis of a) major depressive episode, b) posttraumatic stress disorder, and c) any anxiety disorder (generalized, social phobia, panic) assessed with a structured diagnostic interview in 2002. Demographic characteristics at baseline associated with suicidal behaviour among most subsamples included age, environmental command, and rank. Risk factors at baseline and/or between 2002 and 2018 included prior suicidal behaviour, comorbid mental disorder, child maltreatment, self-medication and avoidance coping, work stress, number of and exposure to traumatic experiences, persistence/recurrence of mental disorder, current comorbid disorder, alcohol use disorder, having released from service, and number of deployment-associated experiences were associated with suicidal behaviour among most subsamples. Protective factors against suicidal behaviour at baseline and/or between 2002 and 2018 included problem-solving coping and social support. Findings identify characteristics of those with mental disorders who may be at greatest risk for developing suicidal behaviour and who need further interventions.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".