Influence of military component and deployment-related experiences on mental disorders among Canadian military personnel who deployed to Afghanistan: a cross-sectional survey
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to explore differences in mental health problems (MHP) between serving Canadian Armed Forces (CAF) components (Regular Force (RegF); Reserve Force (ResF)) with an Afghanistan deployment and to assess the contribution of both component and deployment experiences to MHP using covariate-adjusted prevalence difference estimates. Additionally, mental health services use (MHSU) was descriptively assessed among those with a mental disorder. DESIGN: Data came from the 2013 CAF Mental Health Survey, a cross-sectional survey of serving personnel (n=72 629). Analyses were limited to those with an Afghanistan deployment (population n=35 311; sampled n=4854). Logistic regression compared MHP between RegF and ResF members. Covariate-adjusted prevalence differences were computed. PRIMARY OUTCOME MEASURE: The primary outcomes were MHP, past-year mental disorders, identified using the WHO's Composite International Diagnostic Interview, and past-year suicide ideation. RESULTS: ResF personnel were less likely to be identified with a past-year anxiety disorder (adjusted OR (AOR)=0.72 (95% CI 0.58 to 0.90)), specifically both generalised anxiety disorder and panic disorder, but more likely to be identified with a past-year alcohol abuse disorder (AOR=1.63 (95% CI 1.04 to 2.58)). The magnitude of the covariate-adjusted disorder prevalence differences for component was highest for the any anxiety disorder outcome, 2.8% (95% CI 1.0 to 4.6); lower for ResF. All but one deployment-related experience variable had some association with MHP. The 'ever felt responsible for the death of a Canadian or ally personnel' experience had the strongest association with MHP; its estimated covariate-adjusted disorder prevalence difference was highest for the any (of the six measured) mental disorder outcome (11.2% (95% CI 6.6 to 15.9)). Additionally, ResF reported less past-year MHSU and more past-year civilian MHSU. CONCLUSIONS: Past-year MHP differences were identified between components. Our findings suggest that although deployment-related experiences were highly associated with MHP, these only partially accounted for MHP differences between components. Additional research is needed to further investigate MHSU differences between components.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".