Characterizing the relationship between psychosis and violence in the forensic psychiatric population: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The relationship between psychosis and violence is often construed focusing on a narrow panel of factors; however, recent evidence suggests violence might be linked to a complex interplay of biopsychosocial factors among forensic psychiatric patients with psychosis (FPPP). This review describes violence incidents in FPPP, the factors associated with violence, and relevant implications. METHODS: This review was conducted following the preferred reporting items for systematic reviews and meta-analyses guideline. Databases, including CINAHL, EMBASE, Medline/PubMed, PsycINFO, and Web of Science, were searched for eligible studies that examined violence among adult FPPP. Screening of reports and data extraction were completed by at least two independent reviewers. RESULTS: Across the 29 included studies, violence was consistently related to prior contact with psychiatric services, active psychotic symptoms, impulsivity, adverse experiences, and low social support. However, FPPP who reported violence varied in most other biopsychosocial domains, suggesting the underlying combinatorial effects of multiple risk factors for violence rather than individual factors. Variability in violence was addressed by stratifying FPPP into subgroups using composite/aggregate of identifiable factors (including gender, onset/course of illness, system-related, and other biopsychosocial factors) to identify FPPP with similar risk profiles. CONCLUSIONS: There are multiple explanatory pathways to violence in FPPP. Recent studies identify subgroups with underlying similarities or risk profiles for violence. There is a need for future prospective studies to replicate the clinical utility of stratifying FPPP into subgroups and integrate emerging evidence using recent advancements in technology and data mining to improve risk assessment, prediction, and management.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".