What works to prevent violence against women, domestic abuse and sexual violence (VAWDASV)? A systematic evidence assessment
Bibliographic record
Abstract
This review identifies effective practice for the prevention of violence against women, domestic abuse and sexual violence (VAWDASV). The review is underpinned by public health principles which provide a useful framework to understand the causes and consequences of violence as well as prevention. This systematic evidence assessment had two stages: a database search identified reviews of interventions designed to prevent VAWDASV, published since 2014; a supplementary search identified primary studies published since 2018. Reviews (n=35) and primary studies (n=16) focus on a range of types of violence and interventions. At the individual and relationship level, interventions work to transform harmful gender norms, promote healthy relationships, and promote empowerment. In the community, effective interventions were identified in schools, the workplace, and health settings. Finally, at the societal level, interventions relate to legislation and alcohol policy. The findings reveal a wealth of literature relating to the prevention of VAWDASV. However, gaps in research were identified in relation to the prevention of trafficking, violence against women, domestic abuse, sexual violence among older age groups, and so-called honour-based abuse other than female genital mutilation. Also, while many interventions focus on change at the individual and relationship level and within community settings, there is less evidence for societal-level prevention. The prevention of VAWDASV is both feasible and effective and there is an imperative to invest both in prevention programming and high-quality research to continue to guide efforts to prevent VAWDASV.
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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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".