Life course well-being framework for suicide prevention in Canadian Armed Forces Veterans
Bibliographic record
Abstract
Introduction: The risks of suicidality (suicidal ideation or behaviour) are higher in Canadian Armed Forces (CAF) Veterans (former members) than in the Canadian general population (CGP). Suicide prevention is everyone’s responsibility, but it can be difficult for many to see how they can help. This article proposes an evidence-based theoretical framework for discussing suicide prevention. The framework informed the 2017 joint CAF – Veterans Affairs Canada (VAC) suicide prevention strategy. Methods: Evidence for the framework was derived from participation in expert panels conducted by the CAF in 2009 and 2016, a review of findings from epidemiological studies of suicidality in CAF Veterans released since 1976, suicide prevention literature reviews conducted at VAC since 2009, and published theories of suicide. Results: Common to all suicide theories is the understanding that suicide causation is multifactorial, complex, and varies individually such that factors interact rather than lie along linear causal chains. Discussion: The proposed framework has three core concepts: a composite well-being framework, the life course view, and opportunities for prevention along the suicide pathway from ideation to behaviour. Evidence indicates that Veterans are influenced onto, along, and off the pathway by variable combinations of mental illness, stressful well-being problems and life events, individual factors including suicidal diathesis vulnerability, barriers to well-being supports, acquired lethal capability, imitation, impulsivity, and access to lethal means. The proposed framework can inform discussions about both whole-community participation in prevention, intervention and postvention activities at the individual and population levels, and the development of hypotheses for the increased risk of suicidality in CAF Veterans.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".