INSTRUMENTAL AND REACTIVE VIOLENCE: THE ROLE OF MENTAL HEALTH FACTORS AND MALTREATMENT HISTORY IN THE MANIFESTATION OF VIOLENT OFFENDING
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".