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Record W3035573176 · doi:10.1177/0093854820915740

Is Risk-Need-Responsivity Enough? Examining Differences in Treatment Response Among Male Incarcerated Persons

2020· article· en· W3035573176 on OpenAlexaff
Michael E. Lester, Ashley B. Batastini, Riley M. Davis, Guy Bourgon

Bibliographic record

VenueCriminal Justice and Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsRecidivismPsychologyClinical psychologyHuman factors and ergonomicsInjury preventionPsychiatryPoison controlMedicineMedical emergency

Abstract

fetched live from OpenAlex

Research examining the efficacy of cognitive behavioral therapy (CBT) in reducing recidivism has paid little attention to treatment factors contributing to response variability. Using an archival sample of 448 participants exposed to a risk-need-responsivity (RNR)-informed CBT program or no treatment, a multigroup latent profile analysis yielded a four-profile solution: a treatment-nonresponsive group and three treatment-responsive groups. Among the treatment-responsive profiles, reduced criminal attitudes were most predictive of desistance from reoffending. Elevated rates of recidivism and negligible gains following treatment were associated with pretreatment elevations in antisocial traits, risk level, and negative attitudes toward treatment. These findings underscore a greater need for individualized assessment of risk and treatment motivation, the importance of altering criminal sentiments to prevent reentry into the system upon release, and challenge the idea that 200 hours of treatment is sufficient for lasting change. Study limitations and further directions are discussed, including the need for correctional treatment outcome research to better isolate individual differences.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.133
GPT teacher head0.351
Teacher spread0.219 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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