Intimate Partner Violence (IPV) in Military and Veteran Populations: A Systematic Review of Population-Based Surveys and Population Screening Studies
Bibliographic record
Abstract
Intimate partner violence (IPV) may be a major concern in military and veteran populations, and the aims of this systematic review were to (1) provide best available estimates of overall prevalence based on studies that are most representative of relevant populations, and (2) contextualise these via examination of IPV types, impacts, and context. An electronic search of PsycINFO, CINHAL, PubMed, and the Cochrane Library databases identified studies utilising population-based designs or population screening strategies to estimate prevalence of IPV perpetration or victimisation reported by active duty (AD) military personnel or veterans. Random effects meta-analyses were used for quantitative analyses and were supplemented by narrative syntheses of heterogeneous data. Thirty-one studies involving 172,790 participants were included in meta-analyses. These indicated around 13% of all AD personnel and veterans reported any recent IPV perpetration, and around 21% reported any recent victimisation. There were higher rates of IPV perpetration in studies of veterans and health service settings, but no discernible differences were found according to gender, era of service, or country of origin. Psychological IPV was the most common form identified, while there were few studies of IPV impacts, or coercive and controlling behaviours. The findings demonstrate that IPV perpetration and victimisation occur commonly among AD personnel and veterans and highlight a strong need for responses across military and veteran-specific settings. However, there are gaps in understanding of impacts and context for IPV, including coercive and controlling behaviours, which are priority considerations for future research and policy.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".