Intervening to Prevent Repeat Offending Among Moderate- to High-Risk Domestic Violence Offenders
Bibliographic record
Abstract
Clear directions about best strategies to reduce recidivism among domestic violence offenders have remained elusive. The current study offers an initial evaluation of an RNR (Risk, Needs, and Responsivity)-focused second-responder program for men accused of assaulting their intimate partners and who were judged as being at moderate to high risk for re-offending. A quasi-experimental design was used to compare police outcomes for 40 men attending a second-responder intervention program to 40 men with equivalent levels of risk for re-offense who did not attend intervention (comparison group). Results showed that there were significant, substantial, and lasting differences across groups in all outcome domains. In terms of recidivism, rates of subsequent domestic-violence-related changes were more than double for men in the comparison group as compared with the intervention group in both 1-year (65.9% vs. 29.3%) and 2-year (41.5% vs. 12.2%) follow-up. Changes in the rates of arrest were consistent with reductions in men's general involvement with police, with men in the intervention group receiving fewer charges for violent offenses, administrative offenses, and property offenses over the 2 years following intervention than men in the comparison group. Not surprisingly, these differences result in a much lower estimated amount of police time with intervention men than for comparison men. Results are discussed with reference to the possible impact of sharing information with men about their assessed risk for re-offending within a therapeutic justice context.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".