Criminogenic Needs Among Men Who Perpetrate Intimate Partner Violence: Association with Risk Management Recommendations and Recidivism
Bibliographic record
Abstract
Background Understanding criminogenic needs is important for risk management of intimate partner violence (IPV).Method We analyzed criminogenic needs and management recommendations in 300 men charged for IPV.Results Case files mentioned five criminogenic needs, from antisocial personality (50%) to family/marital problems (94%). Total needs positively correlated with risk management recommendations and substance use positively correlated with IPV recidivism. Needs did not predict recidivism above Ontario Domestic Assault Risk Assessment (ODARA) scores. Risk management recommendations did not reduce the ODARA’s predictive effect.Conclusions We found initial evidence for need principle adherence. Future research should improve criminogenic need measurement and examine risk management implementation.Impact Statement Criminogenic needs are risk factors that are changeable through intervention. In this study, criminogenic needs were common in a high-risk sample of men who perpetrated intimate partner violence. Service providers made more recommendations for managing risk when more criminogenic needs were present; however, there was limited matching of proposed management strategies to specific criminogenic needs. Improvements to the assessment and management of criminogenic needs could help reduce intimate partner violence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".