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Record W2464470396 · doi:10.1080/14999013.2016.1193073

Aggression and Social Anxiety Are Associated with Sexual Offending Against Children

2016· article· en· W2464470396 on OpenAlexaff
Thijs Kanters, Ruud H. J. Hornsveld, Kevin L. Nunes, Almar J. Zwets, Nicole Buck, Peter Muris, H.J.C. van Marle

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
Fundersnot available
KeywordsHostilityAggressionAngerPsychologyAnxietyClinical psychologyPsychiatryChild sexual abuseSexual abusePoison controlSocial anxietyInjury preventionMedicineMedical emergency

Abstract

fetched live from OpenAlex

The current study examined a number of risk factors that are thought to be related to sexual offending. More specifically, we investigated differences in self-reported aggression, anger, hostility, social anxiety, and social skills between child sexual abusers ( n = 28), rapists ( n = 36), and nonsexual violent offenders ( n = 59) who were detained under hospital order. In addition, differences between inpatient ( n = 28) and outpatient child sexual abusers ( n = 61) on the pertinent constructs were evaluated. Consistent with our expectations, we found that child sexual abusers reported themselves as lower on the aggression-related measures and higher on social anxiety than nonsexual violent offenders. In contrast with our hypotheses, however, the results also indicated that the inpatient child sexual abusers reported lower levels of aggression, anger, hostility, and social anxiety than the outpatient child sexual abusers. The observed differences between child sexual abusers, rapists, and nonsexual violent offenders are generally consistent with theories about the etiology of sexual abuse. The differences between the inpatient and outpatient child sexual abusers were not in the expected direction, but may be due to a number of methodological limitations of this research.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.322
Teacher spread0.301 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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