Twelve Month Use of Mental Health Services in a Nationally Representative, Active Military Sample
Bibliographic record
Abstract
BACKGROUND: Mental disorders constitute a significant public health problem in active military populations. However, very little is known about patterns of mental health service use in these populations. OBJECTIVES: The primary objective of this study was to examine the patterns and predictors of mental health service use in active Canadian Force members. Additional objectives included identification of barriers to service use. DESIGN: A cross-sectional analysis was conducted using data from the Canadian Community Health Survey-Canadian Forces Supplement. SUBJECTS AND MEASURES: Participants were assessed for mood, anxiety, and substance use disorders using the World Health Organization's Composite International Diagnostic Interview. Those who met criteria for at least 1 disorder in the past year (n = 1220) were included in the analyses. RESULTS: Of military members with a 12-month diagnosis, 42.6% used services in the past year. Predictors of service use included mental health indicators, gender, marital status, and military rank. Of military members who failed to use services, only a small percentage (3.5-16.0%) acknowledged a need for services. These members perceived a number of barriers to services, foremost among which was lack of trust in military health, administrative, and social services. CONCLUSIONS: Despite recent efforts to de-stigmatize mental health problems and treatments, unmet need for mental health services remains a significant problem in active militaries. Our findings indicate that military institutions should continue public education campaigns to de-stigmatize mental health problems and should make necessary changes in health delivery systems to gain the trust of military members.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".