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Record W4200207803 · doi:10.1037/lhb0000463

Dynamic risk factors reassessed regularly after release from incarceration predict imminent violent recidivism.

2021· article· en· W4200207803 on OpenAlexaff
Ariel Stone, Caleb D. Lloyd, Ralph C. Serin

Bibliographic record

VenueLaw and Human Behavior · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecidivismRisk assessmentPsychologyPoison controlInjury preventionImprisonmentSuicide preventionDemographyPsychiatryClinical psychologyMedicineEmergency medicineCriminologyComputer security

Abstract

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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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.289
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2021
Admission routes1
Has abstractyes

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