Factors associated with mental health in Canadian Veterans
Bibliographic record
Abstract
Introduction: Mental health of Veterans remains a key public policy issue as Veterans with mental health conditions continue to rise in numbers. There is, however, limited information available about specific factors that are associated with mental health in the Veteran population in Canada despite the increasingly perilous nature of military engagements in recent decades. Methods: Regression analysis was conducted on data from a comprehensive self-reported health survey of Canadian Veterans to identify factors associated with mental health, which encompass post-traumatic stress disorders, anxiety disorders, depression, and mood disorders. Results: The findings uncover the role of service-oriented risk factors in the occurrence of mental health conditions notably, overseas deployment (OR=1.55, p≤0.001) and, to a limited extent, land forces (OR=1.34, p≤0.05). The results also show an inverse relationship between income and mental health. Further, lower-educated Veterans have increased odds of mental health conditions. Obesity was found to be a statistically significant factor associated with mental health (OR=1.45, p≤0.001) as well as smoking (OR=1.76, p≤0.001). Home ownership appears to have some protective effect on Veterans' health (OR=1.57, p≤0.001). Discussion: These findings highlight key important factors associated with mental health in Veterans, and they include overseas deployment, land forces enlistment, income, obesity, and smoking. The findings highlight the need for targeted research on the complex causal pathways leading to mental health conditions, especially in deployed Veterans and land forces Veterans so that effective prevention programs can be designed for these groups.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".