Sex Differences in Correlates of Risk and Resilience Associated with Military Sexual Trauma
Bibliographic record
Abstract
Military sexual trauma (MST) is associated with a range of negative mental and physical health outcomes. Investigations of potential sex-based differences in outcomes in MST survivors have been inconsistent with little work evaluating psychosocial correlates of resilience (e.g., social support, humor, capacity to adapt to change). Data were analyzed from 115 U.S. Veterans reporting a history of MST who participated in the nationally representative National Health and Resilience Veterans Study (NHRVS) to examine sex-based correlates of risk and resilience. Compared with female MST survivors (n = 56; 42.9%), male MST survivors (n = 59; 57.1%) reported increased lifetime traumatic events, hostility, and history of drug use disorder, whereas female veterans reported increased lifetime PTSD symptoms. There were no differences in past-month PTSD symptom severity, physical health, suicidal ideation/attempts, resilience factors, or rates of mental health treatment utilization. Results suggest male and female MST survivors may have differential risk for drug use disorder, hostility, and PTSD symptoms. Results further underscore the importance of considering sex differences in the assessment and treatment of MST survivors. Improved understanding of outcomes and factors of risk and resilience in MST survivors may facilitate tailored intervention, treatment, outreach, and possibly prevention.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".