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Record W4415131224 · doi:10.1002/cbm.70014

The Sexual Recidivism Rates of Women Are Still Low: An Updated Meta‐Analysis

2025· article· en· W4415131224 on OpenAlexaff
R. Karl Hanson, Franca Cortoni, Jeffrey C. Sandler

Bibliographic record

VenueCriminal Behaviour and Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de MontréalCarleton University
FundersNew York State Office of Mental Health
KeywordsRecidivismPsychological interventionPublic healthHuman factors and ergonomicsPoison controlInjury preventionSex offense

Abstract

fetched live from OpenAlex

BACKGROUND: Compared to men, women are less likely to sexual offend. Previous reviews found low rates of sexual recidivism among women. The last published meta-analysis was based on studies from before 2010. AIMS: Conduct an updated meta-analysis of the sexual recidivism rates of women returned to the community. We expected the rates to be low and to decline the longer they remained sexual offence free in the community. METHODS: Fourteen studies met selection criteria. Their publication/presentation dates ranged from 1998 to 2023. Results were presented as raw proportions as well as meta-analytic averages. RESULTS: Of the 4208 women, 3.1% (131) were known to have sexually reoffended. The rate was 2.4% during the first 5 years (64/2642, k = 8) and 1.1% between years 5 and 10 (6/535, k = 2). There was large and significant variability across studies (prediction intervals: < 0.001%-11%). The rates of violent recidivism (7.8%) and general (any) recidivism (30.1%) were substantially higher than the rate of sexual recidivism. CONCLUSIONS: This review confirms previous findings that the sexual recidivism rate of women is very low. Their risk is so low that it is unlikely to be reduced by sexual crime specific treatment or public protection measures (e.g., registration and notification). Instead, gender-responsive interventions should focus on the women's risk for general criminal recidivism and strive to promote successful reintegration.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.066
GPT teacher head0.404
Teacher spread0.337 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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