Do Domestic Violence Courts Work? A Meta-Analytic Review Examining Treatment and Study Quality
Bibliographic record
Abstract
Domestic violence courts (DVCs) have become an increasingly popular model in the problem-solving court system. To date, there have been no efforts to summarize the extant literature regarding the impact of DVCs on recidivism. The present study is a meta-analysis of 20 DVC outcome studies reporting on 26 unique DVC samples. The results indicated that DVCs are having a positive impact (i.e., lower odds) on general recidivism (odds ratio [ OR ] = .81, 95% CI [0.68, 0.98], k = 18) as well as domestic violence recidivism ( OR = .81, 95% CI [0.67, 0.97], k = 21), compared to domestic violence offenders processed through the traditional court system. These results, however, became nonsignificant when considering studies of sound methodological quality (as assessed by the Collaborative Outcome Data Committee guidelines). The study also conducted a preliminary investigation of treatment quality (adherence to risk, need, and responsivity [RNR] principles) in the DVC literature. The results indicated that adherence to the RNR principles was low but significantly related to greater treatment effects. Future research should aim to increase the quality of evaluation designs and the courts should look to existing offender rehabilitation literature to inform best practices with domestic violence offenders.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| 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".