Deployment-related trauma and post-traumatic stress disorder: does gender matter?
Bibliographic record
Abstract
Objective: Military research has attempted to identify whether women have an increased vulnerability to mental health issues following deployment-related trauma, but findings have been mixed. Most studies have controlled for childhood abuse, but not other non-deployment trauma (e.g. life-threatening illness), which may partly explain previous mixed results. This study assessed gender differences in the association between deployment-related trauma and post-traumatic stress disorder (PTSD) while controlling for non-deployment trauma.Methods: Data came from the 2013 Canadian Forces Mental Health Survey. Regular or reserve personnel who had been deployed at least once were included in this study (n = 5980). Logistic regression was used to examine the interaction between gender and deployment-related trauma in predicting lifetime PTSD.Results: After controlling for non-deployment trauma, the association of gender with PTSD went from being significant to being marginally significant. The interaction between gender and deployment-related trauma was not significant.Conclusion: Though controlling for non-deployment trauma did not completely dissipate gender differences in PTSD, such differences were greatly reduced, indicating that these may be partly related to traumatic experiences outside deployment. As gender did not moderate the link between deployment-related trauma and PTSD, the findings suggest that trauma experienced while on deployment does not disproportionately affect women compared to their male counterparts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".