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Verbal Intelligence and Criminal Offending Among Men With Schizophrenia

2005· article· en· W2042518502 on OpenAlexaffabout
Åsa Eriksson, Sheilagh Hodgins, Anders Tengström

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2005
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPsychologySchizoaffective disorderPsychiatrySchizophrenia (object-oriented programming)Intelligence quotientPopulationClinical psychologyPsychosisMedicineCognition

Abstract

fetched live from OpenAlex

There are well-documented associations between low verbal IQ and criminal offending and between low verbal IQ and early-onset persistent offending among males in the general population. This study was designed to test whether similar associations could be observed in a sample of 219 men with schizophrenia and schizoaffective disorder. The participants were recruited at discharge from forensic and general psychiatric hospitals in four sites in Canada, Finland, Germany and Sweden. In the weeks preceding discharge, information was collected from participants, family members, staff, and official records. The results demonstrated that low verbal IQ was not associated with the risk of criminal offending, nor with the numbers of offences, but with the age at first non-violent offence. Low verbal IQ was associated with lengthy stays in hospital that may have limited further criminal offending.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.344
Teacher spread0.315 · 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 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

Citations14
Published2005
Admission routes2
Has abstractyes

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