Prevalence and Correlates of Mental Health Problems in Canadian Forces Personnel Who Deployed in Support of the Mission in Afghanistan: Findings from Postdeployment Screenings, 2009–2012
Bibliographic record
Abstract
OBJECTIVE: An important minority of military personnel will experience mental health problems after overseas deployments. Our study sought to describe the prevalence and correlates of postdeployment mental health problems in Canadian Forces personnel. METHOD: Subjects were 16 193 personnel who completed postdeployment screening after return from deployment in support of the mission in Afghanistan. Screening involved a detailed questionnaire and a 40-minute, semi-structured interview with a mental health clinician. Mental health problems were assessed using the Patient Health Questionnaire and the Posttraumatic Stress Disorder Checklist-Civilian Version. Logistic regression was used to explore independent risk factors for 1 or more of 6 postdeployment mental health problems. RESULTS: Symptoms of 1 or more of 6 mental health problems were seen in 10.2% of people screened; the most prevalent symptoms were those of major depressive disorder (3.2%), minor depression (3.3%), and posttraumatic stress disorder (2.8%). The strongest risk factors for postdeployment mental health problems were past mental health care (adjusted odds ratio [AOR] 2.89) and heavy combat exposure (AOR 2.57 for third tertile, compared with first tertile). These risk groups might be targeted in prevention and control efforts. In contrast to findings from elsewhere, Reservist status, deployment duration, and number of previous deployments had no relation with mental health problems. CONCLUSIONS: An important minority of personnel will disclose symptoms of mental health problems during postdeployment screening. Differences in risk factors seen in different nations highlight the need for caution in applying the results of research in one population to another.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".