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Record W4385503483 · doi:10.1177/00938548231186164

Community-Based Treatment for Individuals Convicted of Sexual Offenses Using the Integrated Risk Assessment and Treatment System Model Versus Standard Correctional Programming

2023· article· en· W4385503483 on OpenAlexaffabout
Jeffrey Abracen, Janice Picheca, Jan Looman, Tania Stirpe, Leigh Harkins

Bibliographic record

VenueCriminal Justice and Behavior · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMinistry of Community Safety and Correctional ServicesOntario Tech University
Fundersnot available
KeywordsRecidivismSex offenderRisk assessmentSex offensePsychologyPsychiatryPoison controlSuicide preventionHuman factors and ergonomicsInjury preventionClinical psychologyMedicineSexual abuseMedical emergencyComputer securityComputer science

Abstract

fetched live from OpenAlex

The present investigation examined a group of 90 clients receiving treatment for issues related to sexual offending in The Central District (Ontario) Sex Offender Treatment Program operated by Correctional Service Canada (CSC). Treatment was provided in line with the Integrated Risk Assessment and Treatment System (IRATS) Model developed by the authors. A group of 55 individuals who had not received sexual offense–specific treatment but who received standard correctional programming offered by paraprofessionals were used as a basis of comparison. Risk assessment data were available for all clients included in the analyses. Results indicated that there were no differences between groups on the RRASOR (an actuarial instrument designed to assess risk of sexual offense recidivism) with reference to sexual offender recidivism risk. With reference to sexual offense recidivism, only one of the treated clients recidivated sexually over 8.17 years of follow-up versus four of the 55 comparison group who were followed for a significantly shorter period of time (i.e., 6.9 years).

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.199
GPT teacher head0.427
Teacher spread0.228 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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