Exploring interventions for men and fathers who perpetrate violence against women and children: A scoping review
Bibliographic record
Abstract
BACKGROUND: Violence against women and children -including physical, sexual, and emotional abuse-remains a significant public health issue and frequently co-occurs within families. Although several interventions have been developed for men and fathers who perpetrate violence against woman and children, many do not assess their impact on children. Despite clear evidence of the harmful effects of violence on children's wellbeing, there is limited research examining how these intervention influence child outcomes. OBJECTIVE: This scoping review aimed to assess the impact of IPV intervention programs on child outcomes. METHODS: To identify studies, a comprehensive search strategy was developed using relevant subject headings and keywords. It was executed by a research librarian in July 2024 across seven databases and yielded 4493 studies. After screening references, four articles met the inclusion criteria of this review. Three studies reported improved child outcomes, with child ages ranging from 0 to 18 years old. Three studies used clinician- and parent-reported questionnaires, while the fourth conducted qualitative interviews with children and youth. Four programs were evaluated: Caring Dads: Safer Children (n = 38), Keeping Safe Together (n = 8), and Building Strong Families (n = 3045 fathers), and one unnamed program (n = 138). CONCLUSION: The findings highlight a significant lack of research on the impact of IPV intervention programs on child outcomes. Further research will better inform policy makers, researchers, and clinicians on developing and adjusting the content and structure of intervention programs to effectively support both fathers and their children.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".