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Record W2099131664 · doi:10.1007/s10979-007-9122-8

Relation of antisocial and psychopathic traits to suicide-related behavior among offenders.

2007· article· en· W2099131664 on OpenAlexaff
Kevin S. Douglas, Scott O. Lilienfeld, Jennifer L. Skeem, Norman G. Poythress, John F. Edens, Christopher J. Patrick

Bibliographic record

VenueLaw and Human Behavior · 2007
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
FundersNational Institute of Mental Health
KeywordsPsychopathyAntisocial personality disorderPsychologyImpulsivityPsychopathy ChecklistEmotionalityClinical psychologyDevelopmental psychologyPoison controlPersonalityInjury preventionSocial psychology

Abstract

fetched live from OpenAlex

Offenders with antisocial traits are relatively likely to attempt suicide, largely because they are more likely to have high negative emotionality and low constraint. Among 682 male offenders, we tested whether negative emotionality, low constraint, and also substance use problems mediated any relationship between antisocial personality disorder (ASPD) and psychopathy on the one hand, and suicide-related behavior (SRB) and ideation on the other. ASPD and the impulsivity/lifestyle features of psychopathy weakly predicted SRB. High negative emotionality and low constraint (but not substance use) mediated the relation between ASPD and SRB. Impulsivity/lifestyle features of psychopathy retained an independent predictive effect. Self-report psychopathy measures added unique predictive variance to the Psychopathy Checklist-Revised. We discuss implications for suicide risk assessment and prevention.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.343
Teacher spread0.310 · 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

Citations104
Published2007
Admission routes1
Has abstractyes

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