Predicting self- and other-directed violence among discharged psychiatric patients: the roles of anger and psychopathic traits
Bibliographic record
Abstract
BACKGROUND: We examined the extent to which trait anger and psychopathic traits predicted post-discharge self-directed violence (SDV) and other-directed violence (ODV) among psychiatric patients. METHOD: Participants were 851 psychiatric patients sampled from in-patient hospitals for the MacArthur Violence Risk Assessment Study (MVRAS). Participants were administered baseline interviews at the hospital and five follow-up interviews in the community at approximately 10-week intervals. Psychopathy and trait anger were assessed with the Psychopathy Checklist: Screening Version (PSC:SV) and the Novaco Anger Scale (NAS) respectively. SDV was assessed during follow-ups with participants and ODV was assessed during interviews with participants and collateral informants. Psychopathy facets and anger were entered in logistic regression models to predict membership in one of four groups indicating violence status during follow-up: (1) SDV, (2) ODV, (3) co-occurring violence (COV), and (4) no violence. RESULTS: Anger predicted membership in all three violence groups relative to a non-violent reference group. In unadjusted models, all psychopathy facets predicted ODV and COV during follow-up. In adjusted models, interpersonal and antisocial traits of psychopathy predicted membership in the ODV group whereas only antisocial traits predicted membership in the COV group. CONCLUSIONS: Although our results provide evidence for a broad role for trait anger in predicting SDV and ODV among discharged psychiatric patients, they suggest that unique patterns of psychopathic traits differentially predict violence toward self and others. The measurement of anger and facets of psychopathy during discharge planning for psychiatric patients may provide clinicians with information regarding risk for specific types of violence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".