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Record W4392947618 · doi:10.1002/bsl.2654

Therapeutic and risk relevance of psychopathy and general criminal attitude change in an institutional sexual offense treatment program

2024· article· en· W4392947618 on OpenAlexafffund
Carissa M. Augustyn, Mark E. Olver

Bibliographic record

VenueBehavioral Sciences & the Law · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of SaskatchewanRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecidivismPsychopathyPsychologyPsychopathy ChecklistPoison controlClinical psychologyInjury preventionPsychiatryAntisocial personality disorderPersonalitySocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

We examined the interrelationships between psychopathy, changes in general criminal attitudes, and community recidivism in a sample of 212 men who attended an institutional sexual offense treatment program (SOTP) and were followed for an average of 12.73 years post-release. The men completed a self-report measure of general criminal attitudes, the Criminal Sentiments Scale, as part of routine SOTP service delivery, Psychopathy Checklist-Revised (PCL-R) ratings were completed via file review, and recidivism data were obtained from official criminal records. Criminal attitude endorsement and criminal attitude change had clinically meaningful, but differential, associations with the antisocial and interpersonal features of psychopathy. Further, positive changes in criminal attitudes-particularly tolerance of law violations (i.e., rationalizations for criminal behavior)-were significantly predictive of reductions in community violent and general recidivism after controlling for PCL-R score. Results demonstrate that general criminal attitude change has risk relevance in the treatment of high psychopathy persons with sexual offense histories.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.748

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 studies0.0000.002
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 head0.154
GPT teacher head0.439
Teacher spread0.285 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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