The Role of Psychopathy in Subtypes of Aggression and Gun Violence
Bibliographic record
Abstract
Objective: This study examined the relationship between psychopathy and subtypes of aggression and firearm violence among a high-risk, community-based sample of adults. Specifically, it assessed whether the four-facet model of psychopathy (interpersonal, affective, lifestyle, and antisocial) was differentially associated with reactive and proactive aggression and reactive and proactive gun violence. Additionally, a confirmatory factor analysis (CFA) was conducted to evaluate the factor structure of the Self-Report Psychopathy Short Form (SRP-SF) in this population. Method: = 32.8, SD = 12.8, 72% Male) were included in this study. A CFA assessed the SRP-SF facet structure. Regressions were conducted to determine if psychopathy total and facets scores were associated with reactive and proactive aggression and gun violence. Results: Total psychopathy was associated with reactive and proactive forms of aggression and gun violence. The four-facet model had a good fit. Regressions showed that the affective and lifestyle facets were related to reactive aggression, and the interpersonal and antisocial facets were related to proactive aggression. Higher affective facet scores were associated with increased odds of reactive gun violence, while higher antisocial facet scores were associated with increased odds of proactive gun violence. Conclusion: The findings support the four-facet structure of psychopathy among a high-risk community sample and demonstrate its utility for differentiating violence subtypes. These results highlight the importance of considering psychopathy's multidimensional nature in understanding specific risks for firearm-related violence, providing valuable insights for targeted violence prevention and intervention strategies within healthcare and community settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".