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Record W2917226061 · doi:10.1002/bsl.2394

Evaluation of a cognitive‐behavioral intervention for high‐ and medium‐risk probationers

2019· article· en· W2917226061 on OpenAlexaff
David S. Kosson, Zach Walsh, Michael A. Brook, Marc T. Swogger, Robert Verborg

Bibliographic record

VenueBehavioral Sciences & the Law · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRecidivismPropensity score matchingEthnic groupPsychological interventionIntervention (counseling)Clinical psychologyConfidence intervalPsychologyCognitionMatching (statistics)Relative riskMedicineDemographyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Reducing recidivism is a central goal of treatment programs for offenders. Preliminary evidence suggests that cognitive-behavioral group interventions based on the National Institute of Corrections curriculum (Bush, Glick, & Taymans, 1997) may be effective in reducing recidivism rates among adult probationers. We evaluated the effectiveness of a program based on this curriculum among 167 high- and medium-risk probationers assigned to this program and a comparison group of 120 high- and medium-risk probationers matched on age and number of prior criminal charges. Improvements over prior studies included use of survival analytic methods and propensity score matching, a longer follow-up interval, and examination of treatment effectiveness within ethnic groups. Relative to the comparison group, treatment group probationers were more likely to complete probation satisfactorily and survive longer before rearrest. Moreover, supplementary analyses suggested that ethnicity was associated with differences in intervention effectiveness. Treatment was predictive of lower recidivism rates among European Americans and African Americans but was less effective among Latino American probationers.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.435
Teacher spread0.323 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2019
Admission routes1
Has abstractyes

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