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Record W3025561988 · doi:10.1177/0093854820916774

Interventions for Pedohebephilic Arousal in Men Convicted for Sexual Offenses Against Children: A Meta-Analytic Review

2020· review· en· W3025561988 on OpenAlexafffund
Ian V. McPhail, Mark E. Olver

Bibliographic record

VenueCriminal Justice and Behavior · 2020
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
FundersPublic Safety CanadaUniversity of Saskatchewan
KeywordsPsychological interventionSexual arousalArousalModerationMeta-analysisPsychologyClinical psychologyPoison controlDevelopmental psychologyMedicinePsychiatrySocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

Given the centrality of pedohebephilic interest in understanding sexual offending against children, several interventions have been developed to help men manage or inhibit their sexual arousal to children to reduce the intensity of their experience of such arousal. A meta-analytic review was conducted to examine the effectiveness of interventions for managing pedohebephilic arousal, as measured by phallometric testing. A systematic literature review identified 23 within-group design studies and 18 single-case design studies ( N = 1,071) for analysis. Behavioral and pharmacological interventions showed moderate to large effects for reducing pedohebephilic arousal. Moderator analyses suggest that men with high pretreatment pedohebephilic arousal showed the greatest reductions in arousal. Small effects were found for comprehensive treatment programs; none of the interventions had the effect of increasing sexual arousal to adults. These results support the effectiveness of behavioral and pharmacological interventions for managing pedohebephilic arousal in men convicted of sexual offenses against children.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.012
Bibliometrics0.0050.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.288
GPT teacher head0.468
Teacher spread0.180 · 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 designMeta-analysis
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

Citations22
Published2020
Admission routes2
Has abstractyes

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