Understanding Participant (Dis)Engagement From Domestic Violence Perpetrator Group Programs: A Review and Qualitative Synthesis
Bibliographic record
Abstract
Men’s behavior change programs are a crucial component of contemporary efforts to address domestic violence, particularly (though not exclusively) across the Global North. Yet studies of program effectiveness consistently report high attrition rates and, importantly, an association between non-completion and recidivism. Scholars are thus increasingly concerned with understanding factors that predict program completion. However, limited research examines engagement beyond this binary notion of completion/non-completion. Adopting a broader scope, our systematic review examined English language, peer-reviewed research into the risk and protective factors that may influence engagement with/disengagement from all-male domestic violence perpetrator group programs. Our review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Databases searched include ProQuest, Ovid, EBSCOhost, and Web of Science, from which 164 sources were subject to full-text review, and 80 sources (published 1988–2023) were included. The sources report on studies that use quantitative, qualitative, and mixed methods research designs, based in the United States, Canada, the United Kingdom, Australia, Portugal, and Spain. Through narrative synthesis, we explore how this research conceptualizes completion, considers (or indeed overlooks) engagement, and makes sense of risk/protective factors for non-completion and disengagement. Our findings highlight the inconsistent and often arbitrary definition and measurement of program completion across studies, calling into question its usefulness as a proxy for program effectiveness. Most crucially, our findings support the need for more nuanced analyses of program outcomes that capture the non-linear, relational process with which people engage in perpetrator programs, and behavior change more broadly.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".