Effectiveness of community mobilisation and group-based interventions for preventing intimate partner violence against women in low- and middle-income countries: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) is a challenge affecting one in three women in their lifetime, and gender-transformative interventions have been identified as a promising prevention strategy. We systematically reviewed and meta-analysed randomised controlled trials (RCTs) of community-level or group-based interventions to prevent IPV in lower- and middle-income countries, seeking to answer the following research question: do community- or group-based gender-transformative interventions reduce IPV, compared to a control arm of status-quo programming? Methods: We conducted a systematic search from the inception of all databases employed until 20 July 2021. Eligible study outcomes included past-year experience of physical, sexual, emotional or economic IPV self-reported by women and perpetration of physical or sexual IPV self-reported by men. We assessed study risk of bias using the updated Cochrane tool for RCTs. We estimated the pooled odds ratio (OR) using a multilevel random-effects meta-analysis and also conducted a multilevel meta-regression to analyse how study characteristics moderated the effect size. Results: = 83%), potentially reflecting the diverse contexts of the included trials, our meta-regression did not indicate a significant association between intervention effectiveness and intervention type or target population. There was evidence of significant associations between effectiveness and intervention components and duration. Discussion: There is strong evidence that community-level and group-based interventions reduce IPV against women. Unpacking what intervention modalities are effective in which contexts can further inform prevention strategies. Registration: PROSPERO (CRD42021290193).
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".