Comparison of past-year mental health services use in Canadian Army, Navy, and Air Force personnel
Bibliographic record
Abstract
Introduction: What causes the excess burden of mental disorders and related outcomes in the Army remains unclear. Deployment-related trauma has been one intuitive explanation. However, there may be other factors at play – for example, lower mental health services use (MHSU) in Army personnel. This study compares MHSU across the Canadian Army, Navy, and Air Force. Methods: Data were drawn from the 2013 Canadian Forces Mental Health Survey. The sample consisted of Regular Force members ( N = 6,696). The primary outcomes for past-year MHSU were: (1) any past-year MHSU; (2) intensity of care (total clinical contact hours), and (3) perceived helpfulness of care (PHC). Modified Poisson regression and analysis of covariance (ANCOVA) were used to assess the relationship between the elements (Army, Navy, Air Force) and each outcome, adjusting for sociodemographic and military characteristics, as well as clinical variables such as the presence of five past-year mental disorders. Results: In unadjusted analyses, Army personnel had significantly greater past-year MHSU and intensity of care relative to Air Force personnel. No significant relationship was found between the element and any of the MHSU parameters after adjustment. Discussion: Differences in past-year MHSU are an unlikely contributing factor to the higher risk of mental disorders and related outcomes among Army personnel; the true explanation must lie elsewhere. Findings argue for a system-wide, and not element-specific, approach to improving Canadian Armed Forces (CAF) programs and services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".