Are military personnel with a past history of mental health care more vulnerable to the negative psychological effects of combat?
Bibliographic record
Abstract
Introduction: Military clinicians often need to assess fitness for duty after a mental disorder diagnosis. The ability to respond to the psychological demands of deployment is a primary consideration. This analysis explores whether personnel with past mental health problems are more vulnerable to the effects of combat. Methods: Data came from 16,944 Canadian Armed Forces personnel undergoing post-deployment screening in 2009–2012 after deployment in support of the mission in Afghanistan. Those who had previous deployments ( n = 9,852) and those who were currently in mental health care ( n = 588) were excluded, leaving 6,504 in the analysis sample. The primary outcomes were the presence of one or more of six common mental health problems assessed by the screening questionnaire and the SF-36 Health Survey Mental Component Summary (MCS), a dimensional measure of general mental health. Logistic and linear regression were used to assess the interaction between past mental health care (a proxy for past mental health) and a 30-item combat exposure scale. Results: Past mental health care and combat were strongly and independently associated with both primary outcomes, but no statistically significant interaction was seen for either. Discussion: The effects of past mental health and combat on post-deployment mental health are simply additive. Those with past mental health problems are not, on average, more vulnerable to the effects of combat. The variability in outcome at the individual level and the treatability of common mental disorders argue for an individualized approach to fitness-for-duty decisions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".