Sexual intimate partner violence as a form of MST: An initial investigation.
Bibliographic record
Abstract
Military sexual trauma (MST) is known to impact women's health, but little is known about the occurrence of MST perpetrated by a past or current intimate partner. This study identified the occurrence of intimate partner violence (IPV)-related MST in a sample of female veterans. We also examined the associations between MST history (no MST history, IPV-related MST, and MST by a nonintimate partner) and mental and physical health symptoms. Participants were 369 female veteran patients of Department of Veteran Affairs (VA) facilities in the New England region of the United States who completed a larger 2012 mail survey that included validated assessments of MST, posttraumatic stress disorder (PTSD Checklist) and depressive symptoms (CES-D), and general physical and mental health functioning (Short Form-36). Approximately half (49%) of the women in this sample reported a history of MST, of which 27 (15%) were categorized as IPV-related MST. Few differences in health measures were observed among women with IPV-related MST compared with women who experienced MST by a nonintimate partner or women with no MST history. However, women who experienced IPV-related MST had similarly severe health symptoms as women who reported MST by a nonintimate partner and more severe PTSD symptoms than women without a history of MST. Some women veterans have experienced MST at the hands of an intimate partner and face health impacts. This topic warrants additional attention in clinical and research 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".