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Record W2172281350 · doi:10.1177/1079063214549260

A Prospective Investigation of Factors That Predict Desistance From Recidivism for Adolescents Who Have Sexually Offended

2014· article· en· W2172281350 on OpenAlexaff
James R. Worling, Calvin M. Langton

Bibliographic record

VenueSexual Abuse · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsToronto Metropolitan UniversityActive Healthy Kids
Fundersnot available
KeywordsRecidivismPsychologyClinical psychologyPredictive validitySex offenseScale (ratio)Poison controlDevelopmental psychologyHuman factors and ergonomicsSexual abuseMedicineMedical emergency

Abstract

fetched live from OpenAlex

Current approaches to violence risk assessment are focused on the identification of factors that are predictive of future violence rather than factors that predict desistance. This is also true for the popular tools designed to predict adolescent sexual recidivism. Research on strengths-based variables with adolescents who have sexually offended that could serve a protective function is only recently underway. In the current prospective study, scores from clinician-completed assessments using the Estimate of Risk of Adolescent Sexual Offense Recidivism (ERASOR) and the parent-completed form of the Behavioral and Emotional Rating Scale (BERS-2) were evaluated in a sample of 81 adolescent males with at least one sexual offense. As expected, the ERASOR was significantly correlated with sexual recidivism over an average 3.5-year follow-up. In terms of a protective function, the Affective Strength scale of the BERS-2 was significantly negatively correlated with sexual recidivism, although it did not have incremental validity over and above the ERASOR. The BERS-2 School Functioning scale was significantly negatively correlated with nonsexual recidivism. The results are discussed in terms of previous findings and theoretical work on attachment in sexual offending behavior and implications for risk assessment practice.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.287
Teacher spread0.245 · 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.

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

Citations31
Published2014
Admission routes1
Has abstractyes

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