Stress, Schizophrenia, and Violence: A Machine Learning Approach

Autor(en)
Johannes Kirchebner, Martina Sonnweber, Urs Markus Nater, Moritz Günther, Steffen Lau
Abstrakt

This study employs machine learning algorithms to examine the causes for engaging in violent offending in individuals with schizophrenia spectrum disorders. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy, Zurich University Hospital of Psychiatry, Switzerland. Based on findings of the general strain theory and using logistic regression and machine learning algorithms, it was analyzed whether accumulation and type of stressors in the inpatients' history influenced the severity of an offense. A higher number of stressors led to more violent offenses, and five types of stressors were identified as being highly influential regarding violent offenses. Our findings suggest that an accumulation of stressful experiences in the course of life and certain types of stressors might be particularly important in the development of violent offending in individuals suffering from schizophrenia spectrum disorders. A better understanding of risk factors that lead to violent offenses should be helpful for the development of preventive and therapeutic strategies for patients at risk and could thus potentially reduce the prevalence of violent offenses.

Organisation(en)
Institut für Klinische und Gesundheitspsychologie, Forschungsplattform The Stress of Life - Processes and Mechanisms underlying Everyday Life Stress
Externe Organisation(en)
Psychiatrische Universitätsklinik Zürich (PUZ)
Journal
Journal of Interpersonal Violence
Band
37
Seiten
602-622
Anzahl der Seiten
21
ISSN
0886-2605
DOI
https://doi.org/10.1177/0886260520913641
Publikationsdatum
01-2022
Peer-reviewed
Ja
ÖFOS 2012
501010 Klinische Psychologie
Schlagwörter
ASJC Scopus Sachgebiete
Clinical Psychology, Applied Psychology
Sustainable Development Goals
SDG 16 – Frieden, Gerechtigkeit und starke Institutionen
Link zum Portal
https://ucrisportal.univie.ac.at/de/publications/9012305f-2e82-43fb-8334-fe5e4f8744cc