Contribution of traumatic deployment experiences to the burden of mental health problems in Canadian Armed Forces personnel: exploration of population attributable fractions
Bibliographic record
Abstract
PURPOSE: Mental health problems are prevalent after combat; they are also common in its absence. Estimates of deployment-attributability vary. This paper quantifies the contribution of different subtypes of occupational trauma to post-deployment mental health problems. METHODS: Participants were a cohort of 16,193 Canadian personnel undergoing post-deployment mental health screening after return from the mission in Afghanistan. The screening questionnaire assessed post-traumatic stress disorder, depression, panic disorder, generalized anxiety disorder, and exposure to 30 potentially traumatic deployment experiences. Logistic regression estimated adjusted population attributable fractions (PAFs) for deployment-related trauma, which was treated as count variables divided into several subtypes of experiences based on earlier factor analytic work. RESULTS: The overall PAF for overall deployment-related trauma exposure was 57.5% (95% confidence interval 44.1, 67.7) for the aggregate outcome of any of the four assessed problems. Substantial PAFs were seen even at lower levels of exposure. Among subtypes of trauma, exposure to a dangerous environment (e.g., receiving small arms fire) and to the dead and injured (e.g., handling or uncovering human remains) had the largest PAFs. Active combat (e.g., calling in fire on the enemy) did not have a significant PAF. CONCLUSIONS: Military deployments involving exposure to a dangerous environment or to the dead or injured will have substantial impacts on mental health in military personnel and others exposed to similar occupational trauma. Potential explanations for divergent findings in the literature on the extent to which deployment-related trauma contributes to the burden of mental disorders are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".