Dynamic risk factors reassessed regularly after release from incarceration predict imminent violent recidivism.
Bibliographic record
Abstract
OBJECTIVE: In community-based corrections, reassessment of dynamic risk factors improves the prediction of recidivism relative to initial risk assessment at the time of release. However, there is less evidence for predictions of violent recidivism. We examined whether reassessment proximity or aggregation of reassessments improved the prediction of imminent violence in a sample of paroled individuals on community supervision. HYPOTHESES: We hypothesized that reassessment of dynamic risk would better predict violent recidivism than initial risk assessment at the time of release. Examination of aggregation and individual risk-factor domains was exploratory. METHOD: In a prospective study of violent recidivism in a sample of individuals on community supervision in New Zealand (75,917 assessments from 3,421 participants; 92.8% men), we used supervision officers' ratings of dynamic risk (assessed using Dynamic Risk Assessment for Offender Re-entry [DRAOR]) and static risk scores (using the Risk of ReConviction × Risk of Imprisonment) to predict imminent violence (within 2 weeks). RESULTS: Individuals who recidivated violently had higher initial risk ratings (DRAOR Stable d = 0.36, 95% CI [0.17, 0.55]; DRAOR Acute d = 0.45, 95% CI [0.26, 0.64]) and showed more week-to-week fluctuations in risk ratings (DRAOR Stable d = 0.21, 95% CI [0.04, 0.41]; DRAOR Acute d = 0.26, 95% CI [0.06,0.46]). Total averages of faster-changing acute risk factors best predicted violence (c-index = 0.68), with changes in these factors incrementally predicting violence over well-established predictors (criminal history) and initial scores (Δχ2 = 15.54, df = 3). The constructs that best discriminated violence were consistent with social cognition explanations of violence. CONCLUSIONS: Because client consistency as determined through score aggregation was more important than current presentation, supervision officers should consider overall patterns of interpersonal hostility and reactivity rather than assuming the emerging presence of these factors will signal imminent violence among previously violent individuals. (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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".