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Characterizing the relationship between psychosis and violence in the forensic psychiatric population: a systematic
Angad Singh1, William Pereira1, Sapriya Birk1
1Michael G. Degroote School of Medicine, McMaster University, Hamilton, ON, Canada.
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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