Childhood Maltreatment and Aggressive Behaviour in Violent Offenders with Psychopathy
Bibliographic record
Abstract
OBJECTIVE: To document experiences of childhood maltreatment among violent offenders with antisocial personality disorder (ASPD) distinguishing between those with and without the syndrome of psychopathy (+P and -P), and to determine whether maltreatment is associated with proactive and reactive aggression. METHOD: The sample included 10 violent offenders with ASPD+P, 15 violent offenders with ASPD-P, and 15 non offenders. All participants completed interviews with the same forensic psychiatrist focusing on physical, sexual, and emotional abuse prior to age 18 using the Early Trauma Inventory. Aggression was assessed using the Reactive-Proactive Questionnaire. RESULTS: Violent offenders with ASPD+P reported significantly more severe childhood physical abuse, but not more sexual or emotional abuse, than violent offenders with ASPD-P and non offenders. Psychopathy Checklist-Revised (PCL-R) scores, but not childhood physical abuse, were associated with proactive aggression. Childhood physical abuse was associated with reactive aggression, as was an interaction term indicating that when both PCL-R scores and childhood physical abuse were high, so was reactive aggression. CONCLUSIONS: Among violent offenders, PCL-R scores were positively associated with proactive aggression, while experiences of childhood maltreatment were not. This finding concurs with previous studies of children and adults and suggests that proactive aggression may be a behavioural marker of psychopathic traits. By contrast, childhood physical abuse was associated with reactive aggression, even among violent offenders with high PCL-R scores. This latter finding suggests a strong influence of childhood physical abuse on the development of reactive aggression that persists over the lifespan.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".