Contribution of the Mission in Afghanistan to the Burden of Past-Year Mental Disorders in Canadian Armed Forces Personnel, 2013
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to estimate the contribution of the mission in Afghanistan to the burden of mental health problems in the Canadian Armed Forces (CAF). METHODS: Data were obtained from the 2013 Canadian Forces Mental Health Survey, which assessed mental disorders using the World Health Organization's Composite International Diagnostic Interview. The sample consisted of 6696 Regular Force (RegF) personnel, 3384 of whom had deployed in support of the mission. We estimated the association of past-year mental health problems with Afghanistan deployment status, adjusting for covariates using logistic regression; population attributable fractions (PAFs) were also calculated. RESULTS: Indication of a past-year mental disorder was identified in 18.4% (95% confidence interval [CI], 17.0% to 19.7%) of Afghanistan deployers compared with 14.6% (95% CI, 13.3% to 15.8%) in others. Afghanistan-related deployments contributed to the burden of a past-year disorder (PAF = 8.7%; 95% CI, 3.0% to 14.2%), with the highest PAFs being seen for panic disorder (34.7%) and posttraumatic stress disorder (32.1%). The PAFs for individual alcohol use disorders and suicide ideation were not different from zero. Child abuse, however, had a much greater PAF for any past-year disorder (28.7%; 95% CI, 23.4% to 33.7%) than did the Afghanistan mission. CONCLUSIONS: The mission in Afghanistan contributed significantly to the burden of mental disorders in the CAF RegF in 2013. However, the much stronger contribution of child abuse highlights the need for strong military mental health systems, even in peacetime, and the need to target the full range of determinants of mental health in prevention and control efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".