Severe Mental Illness and Aggressive Behavior: On the Importance of Considering Subgroups
Bibliographic record
Abstract
A minority of persons with severe mental illness (SMI) engage in violence towards others. These individuals, however, constitute a heterogeneous population with respect to patterns and correlates of violence. The present study used multivariate profile analyses to identify clusters of patients with SMI who displayed similar patterns of violence and similar correlates among 178 participants with SMI and a history of interpersonal violence. The types, locations, methods, and victims of violence, the associations of psychotic symptoms, alcohol and drug intoxication with violence, psychopathy traits, and impulsivity were entered into multiple correspondence analyses. Three subgroups of violent individuals were identified: psychotic, repetitive, and institutional. A fourth, unexpected subgroup of less violent persons was also found. The subgroups differed as to frequency, type, method, and location of violent incidents towards others; the presence of delusions, hallucinations, and intoxication when violent; and levels of psychopathy traits and impulsivity. These results demonstrate that among persons with SMI and considered at risk for violence, there are subgroups who display distinctive patterns and correlates of violent behavior. The finding that factors promoting violence in the subgroups differ indicates the need for distinct treatment and management strategies to reduce violence in each type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".