Prevalence and Correlates of Military Sexual Trauma in Service Members and Veterans: Results From the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey
Bibliographic record
Abstract
INTRODUCTION: Military sexual trauma (MST) is an ongoing problem. We used a 2002 population-based sample, followed up in 2018, to examine: (1) the prevalence of MST and non-MST in male and female currently serving members and veterans of the Canadian Armed Forces, and (2) demographic and military correlates of MST and non-MST. METHODS: Data came from the 2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (n = 2,941, ages 33 years + ). Individuals endorsing sexual trauma were stratified into MST and non-MST and compared to individuals with no sexual trauma. The prevalence of lifetime MST was computed, and correlates of sexual trauma were examined using multinomial regression analyses. RESULTS: The overall prevalence of MST was 44.6% in females and 4.8% in males. Estimates were comparable between currently serving members and veterans. In adjusted models in both sexes, MST was more likely among younger individuals (i.e., 33-49 years), and MST and non-MST were more likely in those reporting more non-sexual traumatic events. Among females, MST and non-MST were more likely in those reporting lower household income, non-MST was less likely among Officers, and MST was more likely among those with a deployment history and serving in an air environment. Unwanted sexual touching by a Canadian military member or employee was the most prevalent type and context of MST. INTERPRETATION: A high prevalence of MST was observed in a follow-up sample of Canadian Armed Forces members and veterans. Results may inform further research as well as MST prevention efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".