Predictors of Likelihood and Intensity of Past-Year Mental Health Service Use in an Active Canadian Military Sample
Bibliographic record
Abstract
OBJECTIVE: This study examined associations between sociodemographic, military, and psychiatric need variables and past-year mental health service use among active Canadian military members. The likelihood and intensity of services were examined across two provider types--mental health providers and medical providers. METHODS: Data were drawn from the first epidemiological survey of mental health in the Canadian Forces, conducted by Statistics Canada in 2002. Survey instruments included the Composite International Diagnostic Interview, which was used to assess mental health and service use. RESULTS: Of the 8,441 military members who participated in the survey, 14.5% (N=1,220) met criteria for having a mental disorder in the past year. However, of the 8,441 only 9.1% (N=767) contacted a mental health provider in the past year for mental health problems; even fewer (N=539, 6.4%) contacted a medical provider. Across the two provider types, the majority of those seeing a provider reported five or fewer mental health visits in the past year. In univariate and multivariate analyses across the two provider types, psychiatric need variables were consistently associated with both greater service use likelihood and intensity. In multivariate analyses, lower military rank was consistently associated with both greater service use likelihood and intensity. CONCLUSIONS: Of the entire military sample, only a small percentage used mental health services. The observed associations between military and psychiatric need variables and mental health service use in this study should be used by military health care providers and administrators to increase mental health service use among those most at risk of not using 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".