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Record W2126003763 · doi:10.1177/1524838008319633

Treatment of Sexual Offenders With Psychopathic Traits

2008· review· en· W2126003763 on OpenAlexaff
Jeffrey Abracen, Jan Looman, Calvin M. Langton

Bibliographic record

VenueTrauma Violence & Abuse · 2008
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOntario HIV Treatment NetworkMinistry of Community Safety and Correctional Services
Fundersnot available
KeywordsPsychopathyPsychologyParaphiliaClinical psychologySexual abusePoison controlOptimismPsychiatrySex offenseInjury preventionPsychotherapistPersonalityMedicineSocial psychologyMedical emergencySexual behavior

Abstract

fetched live from OpenAlex

This article provides a review of recent literature related to the treatment of psychopathy in forensic settings, with particular focus on studies with sexual offenders. The lack of empirical support for the position that psychopaths are untreatable is noted, and data suggesting optimism is discussed. Research demonstrating an interaction between psychopathy and substance abuse and between psychopathy and sexual deviance is also presented. Both substance abuse disorders and inappropriate arousal are frequently encountered in high-risk groups of sexual offenders. These topics are of relevance, in that the question of whether treatment is beneficial with high-risk sexual offenders, including those who are psychopathic, cannot be answered unless groups presenting with typical concurrent disorders (paraphilias, substance abuse, dependence) are discussed. It is argued that, although the evidence is still preliminary, there is reason for optimism with reference to whether psychopathic sexual offenders are responsive to treatment, including offenders presenting with concurrent disorders.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.087
GPT teacher head0.363
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2008
Admission routes1
Has abstractyes

Explore more

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