Associations between military sexual trauma and chronic pain in men and women active military members and veterans
Bibliographic record
Abstract
Introduction We evaluated gendered risks and the associations between demographics, anxiety, and depressive symptoms, military sexual trauma (MST), and severe pain in Canadian military personnel and veterans.Methods A cross-sectional sample of 328 veterans and military members with chronic pain completed confidential surveys, including demographics, disability, pain location, and intensity, as well as a Patient Health Questionnaire (PHQ-4). We evaluated gendered exposures (rates, odds ratios), associations between MST and severe pain (logistic regression), and associations with pain severity and PHQ-4 (linear regression), adjusting for age, race, and education.Results We found high levels of chronic pain and disability for women and men (6.8 or 6.9/10), PHQ-4 scores (6.6 or 6.2/12), and SANE scores (11% or 10%). Women reported higher exposure to MST (e.g. 69% for sexual harassment, 76% for emotional abuse) than men (8% and 13%). Odds of severe pain were not gendered (OR = 1.05). Physical abuse (β = 1.09, p = .030) and gender discrimination (β = 1.10, p = .033) were associated with higher PHQ-4 scores. Sexual harassment was not independently associated (OR = 1.57, p = .354) with chronic pain, but was associated with increased anxiety and depressive symptoms (β = 1.06, p = .016). Higher PHQ-4 scores were significantly associated with higher pain scores (β = 0.18, p < .001).Conclusions MST exposures are much more common among women, but the associations of MST with severe pain and PHQ-4 scores are similar across genders. Differences in operational trauma and occupational trauma may influence occupational stress injuries and chronic pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".