What works to prevent violence against women, domestic abuse and sexual violence (VAWDASV)? A systematic evidence assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it