Health Service Interventions for Intimate Partner Violence among Military Personnel and Veterans: A Framework and Scoping Review
Bibliographic record
Abstract
IPV is a significant concern among active duty (AD) military personnel or veterans, and there is a need for initiatives to address violence perpetrated by such personnel, and IPV victimisation in military and veteran-specific contexts. The aim of this paper was to provide an overview of major IPV intervention approaches and evidence in military and veteran-specific health services. A scoping review was conducted involving a systematic search of all available published studies describing IPV interventions in military and veteran-specific health services. Findings were synthesised narratively, and in relation to a conceptual framework that distinguishes across prevention, response, and recovery-oriented strategies. The search identified 19 studies, all from the U.S., and only three comprised randomised trials. Initiatives addressed both IPV perpetration and victimisation, with varied interventions targeting the latter, including training programs, case identification and risk assessment strategies, and psychosocial interventions. Most initiatives were classified as responses to IPV, with one example of indicated prevention. The findings highlight an important role for specific health services in addressing IPV among AD personnel and veterans, and signal intervention components that should be considered. The limited amount of empirical evidence indicates that benefits of interventions remain unclear, and highlights the need for targeted research.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".