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Record W3175588190 · doi:10.1007/s11920-021-01259-3

Does Treatment for Sexual Offending Work?

2021· review· en· W3175588190 on OpenAlexaff
Nichola Tyler, Theresa A. Gannon, Mark E. Olver

Bibliographic record

VenueCurrent Psychiatry Reports · 2021
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsExtant taxonPsychologyPsychological interventionClinical psychologySexual assaultPsychotherapistHuman factors and ergonomicsPoison controlMedicinePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: We review and synthesize the literature on the effectiveness of offense-focused treatment for sexual offending. Specifically, we consider whether the extant literature suggests treatment is effective in reducing sexual reoffending and features of effective interventions. We also consider how the design of program evaluations may influence treatment outcomes. RECENT FINDINGS: Recent research suggests that offense-focused psychological treatment for sexual offending shows some level of effectiveness in reducing both sexual and general reoffending. Further, there appear to be key program, individual, and study design features associated with treatment effectiveness. Although recent findings paint an optimistic outlook for offense-focused psychological treatment for sexual offending, further high-quality differential studies are needed to fully understand the range of content, delivery, and individual factors associated with successful treatment outcomes so as to establish what works best for whom.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.142
GPT teacher head0.439
Teacher spread0.297 · 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 designSystematic review
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

Citations36
Published2021
Admission routes1
Has abstractyes

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