Disorder-specific risk factors of suicidal behaviour among serving and veteran Canadian Armed Forces Members with baseline mental health diagnoses
Bibliographic record
Abstract
OBJECTIVES: Many Canadian Armed Forces (CAF) members and veterans will receive a mental disorder diagnosis, and a high percentage will also experience suicidal behaviours. This study examined demographic characteristics, distal and proximal risk factors, and protective factors, and their relationship to suicidal behaviour (ideation, plans, and attempts) among CAF members and veterans who met criteria for a mental disorder at baseline. METHODS: Data from the 2018 CAF Members and Veterans Mental Health Follow-up Survey (n = 2941) were utilized. Mental disorder diagnoses were assessed through structured diagnostic interview. Generalized linear models were conducted using subsamples of individuals with a lifetime baseline diagnosis of (a) major depressive episode (MDE), (b) posttraumatic stress disorder (PTSD), and (c) an anxiety disorder (AD; social phobia, generalized, panic). RESULTS: Across mental disorder subsamples of those with MDE and AD, land environmental command at baseline was associated with increased prevalence of suicidal behaviour. Risk factors for suicidal behaviour across all subsamples included baseline suicidal behaviour, greater level of self-medication and avoidant coping style, greater level of baseline work stress, greater number of traumatic experiences, persistence or recurrence of index mental disorder, current comorbid mental disorder, current physical health condition, exposure to "other" traumatic experiences, and alcohol use disorder. Protective factors across all subsamples included greater level of current problem-solving coping style. Disorder-specific factors were also identified. CONCLUSION: This study identified characteristics of individuals living with mental disorders who might be at high risk of suicidal behaviour, highlighting potential areas for targeted interventions in this key population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".