Population-Based Approaches to Prevent Domestic Violence against Women Using a Systematic Review
Bibliographic record
Abstract
Objective: In this systematic review, we aimed to evaluate the existing strategies and interventions in domestic violence prevention to assess their effectiveness. Method: To select studies, Pubmed, ISI, CINAHL, PsycINFO, Cochrane, Scopus, Embase, Ovid, Science Direct, ProQuest, and Elsevier databases were searched. Two authors reviewed all papers using established inclusion/ exclusion criteria. Finally, 18 articles were selected and met the inclusion criteria for assessment. Following the Cochrane quality assessment tool and AHRQ Standards, the studies were classified for quality rating based on design and performance quality. Two authors separately reviewed the studies and categorized them as good, fair, and poor quality. Results: Most of the selected papers had fair- or poor-quality rating in terms of methodology quality. Different intervention methods had been used in these studies. Four studies focused on empowering women; 3, 4, and 2 studies were internet-based interventions, financial interventions, and relatively social interventions, respectively. Four interventions were also implemented in specific groups. All authors stated that interventions were effective. Conclusion: Intervention methods should be fully in line with the characteristics of the participants. Environmental and cultural conditions and the role of the cause of violence are important elements in choosing the type of intervention. Interventions are not superior to each other because of their different applications.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".