Criminogenic needs and intimate partner violence: Association with recidivism and implications for treatment.
Bibliographic record
Abstract
The Risk-Need-Responsivity (RNR) principles of effective correctional service that are well supported in the general offending literature have less often been applied to the assessment and treatment of intimate partner violence (IPV). Well validated IPV risk assessment tools are now widely available, and IPV treatment programs that match treatment intensity to assessed risk have shown promising pre-to-post treatment effects. The present study builds on the study of RNR principles in IPV by exploring criminogenic needs and their relation to recidivism and to recently proposed treatment intensity categories derived from an IPV risk assessment tool. We reanalyzed data from 1,421 men with a police report of IPV in the original Ontario Domestic Assault Risk Assessment (ODARA) dataset, to explore the prevalence of antisocial personality traits, procriminal attitudes, substance use, poor relationships, and work/school problems and their relation to IPV recidivism and ODARA-based treatment intensity categories. Needs were present in 17% (procriminal attitudes) to 42% (substance use) of men. All needs except poor relationships were positively related to IPV recidivism; in logistic regression analyses, antisocial personality traits (OR = 1.80) and poor relationships (OR = 0.61) incrementally predicted IPV recidivism over the ODARA (OR = 1.40). Men placed in higher treatment intensity categories based on the risk assessment score had more criminogenic treatment needs. Findings support using the ODARA to select individuals for the most intensive IPV treatment, and suggest that assessing and treating criminogenic needs may improve IPV treatment outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".