Mental health of Canadian Armed Forces Veterans: review of population studies
Bibliographic record
Abstract
Introduction. The mental health of Canadian Armed Forces (CAF) populations emerged as an important concern in the wake of difficult CAF deployments in the 1990s. This article is the first comprehensive summary of findings from subsequent surveys of mental health and well-being in CAF Veterans, undertaken to inform mental health service renewals by CAF Health Services and Veterans Affairs Canada (VAC). Methods. Epidemiological findings in journal publications and government reports were summarized from four cross-sectional national surveys: a survey of Veterans participating in VAC programs in 1999 and three surveys of health and well-being representative of whole populations of Veterans in 2003, 2010, and 2013. Results. Although most Veterans had good mental health, many had mental health problems that affected functioning, well-being, and service utilization. Recent Veterans had a higher prevalence of mental health problems than the general Canadian population, earlier-era Veterans, and possibly the serving population. There were associations between mental health conditions and difficult adjustment to civilian life, physical health, and multiple socio-demographic factors. Mental health problems were key drivers of disability. Comparisons with other studies were complicated by methodological, era, and cultural differences. Discussion. The survey findings support ongoing multifactorial approaches to optimizing mental health and well-being in CAF Veterans, including strong military-to-civilian transition support and access to effective mental and physical health services. Studies underway of transitioning members and families in the peri-release period of the military-to-civilian transition and longitudinal studies of mental health in Veterans will address important knowledge gaps.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.045 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".