Combat and Peacekeeping Operations in Relation to Prevalence of Mental Disorders and Perceived Need for Mental Health Care
Bibliographic record
Abstract
CONTEXT: Although military personnel are trained for combat and peacekeeping operations, accumulating evidence indicates that deployment-related exposure to traumatic events is associated with mental health problems and mental health service use. OBJECTIVE: To examine the relationships between combat and peacekeeping operations and the prevalence of mental disorders, self-perceived need for mental health care, mental health service use, and suicidality. DESIGN: Cross-sectional, population-based survey. SETTING: Canadian military. PARTICIPANTS: A total of 8441 currently active military personnel (aged 16-54 years). MAIN OUTCOME MEASURES: The DSM-IV mental disorders (major depressive disorder, posttraumatic stress disorder, generalized anxiety disorder, panic disorder, social phobia, and alcohol dependence) were assessed using the World Mental Health version of the World Health Organization Composite International Diagnostic Interview, a fully structured lay-administered psychiatric interview. The survey included validated measures of self-perceived need for mental health treatment, mental health service use, and suicidal ideation. Lifetime exposure to peacekeeping and combat operations and witnessing atrocities or massacres (ie, mutilated bodies or mass killings) were assessed. RESULTS: The prevalences of any past-year mental disorder assessed in the survey and self-perceived need for care were 14.9% and 23.2%, respectively. Most individuals meeting the criteria for a mental disorder diagnosis did not use any mental health services. Deployment to combat operations and witnessing atrocities were associated with increased prevalence of mental disorders and perceived need for care. After adjusting for the effects of exposure to combat and witnessing atrocities, deployment to peacekeeping operations was not associated with increased prevalence of mental disorders. CONCLUSIONS: This is the first study to use a representative sample of active military personnel to examine the relationship between deployment-related experiences and mental health problems. It provides evidence of a positive association between combat exposure and witnessing atrocities and mental disorders and self-perceived need for treatment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".