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Record W651030960

INSTRUMENTAL AND REACTIVE VIOLENCE: THE ROLE OF MENTAL HEALTH FACTORS AND MALTREATMENT HISTORY IN THE MANIFESTATION OF VIOLENT OFFENDING

2010· article· en· W651030960 on OpenAlexvenueno aff
Rebecca L. Douglas

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyOccupational safety and healthPoison controlSuicide preventionInjury preventionHuman factors and ergonomicsMedical emergencyCriminologyPsychiatryMedicine
DOInot available

Abstract

fetched live from OpenAlex

Researchers have consistently identified two distinct types of aggression: A “hot-blooded”, impulsive, reactive form of aggression, and a “cold-blooded”, premeditated, instrumental form of aggression. Despite the relevance of psychopathology to the prediction of violent offending, there has been limited research on the role of mental health factors in subtypes of severe criminal violence. Childhood maltreatment history has also demonstrated associations with both psychopathology and violence, yet has not been investigated in subtypes of severe violence in adults. In the current study, the relationships between mental health history, substance use, personality pathology, maltreatment, and subtypes of criminal violence were examined in a sample of 144 incarcerated male offenders. Domain-specific multinomial logistic regression analyses indicated that the likelihood of reactive violence was predicted by the severity of alcohol use history and polysubstance intoxication at the time of the offence. Whereas there was a trend for stimulant use history to be predictive of reactive violence, stimulant intoxication at the time of offence was exclusively associated with instrumental violence. Severity of opiate use history revealed a trend for association with the likelihood of instrumental violence. Specific Axis I mental health problems, personality pathology, and maltreatment history were not predictive of violence subtype. Although psychopathy was not a significant individual predictor of violence subtype, the interaction between substance intoxication and specific psychopathic traits contributed significantly to the prediction of violence subtype. A final logistic regression model identified stimulant intoxication, polysubstance intoxication, and alcohol use history as key predictors of violence subtype. This model allowed for the prediction of subtype of violence at a rate higher than chance. In addition to risk-factor analyses, person-focused analyses identified four clusters of offenders in the current sample: A High Psychopathology cluster, a Low Psychopathology cluster, an Antisocial cluster, and a Moderate Schizoid Traits cluster. Clusters differed significantly on psychopathology profiles, and were marginally different on maltreatment history. However, clusters demonstrated limited association with subtype of violence. Findings from this research have important implications for violence risk prediction, offender profiling, and developing targeted intervention services.

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)0.006
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.005
GPT teacher head0.188
Teacher spread0.182 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)Same topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207