What determines violent behavior in men? Predicting physical, psychological, and sexual violent offending based on classification and regression tree analysis

Abstract

Despite compelling evidence that victimization and offending co-occur, it remains unclear what types of victimization are linked to specific forms of perpetration. Here we examined the relationship between physical, psychological, and sexual violence with respect to influencing variables including mental health, risk-taking behaviors, and coping strategies. Data from 5385 men were collected as part of an epidemiological study on violence experience. A classification and regression tree analysis identified the main predictors of violence perpetration and classified violent offending into high-and low-risk groups.Results indicate that violence is best predicted by previous exposure to violence and polyvictimization. Physical violence is best predicted by prior exposure to physical violence and this is further influenced by the frequency of and the age at which violence was experienced. Drug use was a strong predictor of physical and psychological violence. The latter is best predicted by a history of polyvictimization, the severity and the originator of violence. Sexual violence is strongly predicted by one's sexual violence experience. Other factors such as demographic characteristics are less predictive. Our results may contribute to the development of early prevention and intervention approaches that account for different risk factors. The significance of violence exposure suggest that intervention measures must focus on victims of early and prolonged experience of violence. On the strength of the link between drug use and violence, exposure to violence should be considered in drug prevention and intervention and vice versa. K E Y W O R D S classification and regression tree analysis, exposure to violence, perpetration, polyvictimization, risk factors, violent offending This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Lifetime Prevalence of Victimization and Perpetration as Related to Men’s Health: Clinical Insights

“…Moreover, youths who reported high/moderate levels of various types of violence were also more likely to perpetrate violent behavior ( Debowska et al, 2018 ). Results of Leiding et al (2021) in a clinical study add to these findings and showed that previous exposure to violence and the experience of multiple forms of violence (polyvictimization) predict perpetration. Thus, childhood victimization and attitudes toward violence may contribute to perpetration and/or re-victimization in adulthood.…”

“…The rejection rate was 35.8%. Full details about the sample are reported elsewhere ( Leiding et al, 2021 ). Participants received study information on two flyers.…”

“…Furthermore, the questionnaire includes six items on help-seeking behavior of the participants. For further information about development, piloting, and item details of this prevalence questionnaire, see Leiding et al (2021) .…”

Perfil epidemiológico da mortalidade por causas externas em um estado da região Nordeste do Brasil, de 2010 a 2020

“…Além disso, alguns insumos pessoais e situacionais que aumentam a probabilidade de agressão incluem características demográficas, tendências a comportamentos de risco e experiência anterior com violência. Esses resultados, no entanto, variam devido a diferentes indicadores de nível socioeconômico, a exemplo da baixa renda, níveis de educação e problemas de emprego, em particular, que correlacionam-se com níveis mais altos de violência e vitimização (Daly, 2016;Leiding et al, 2021). falta de estudos epidemiológicos que identifiquem as reais causas de morte, gerando informações para a produção de planos de ações voltados à necessidade específica de cada área (Santos et al, 2022;Segundo et al, 2022;SESAU, 2016).…”

Automatic Decision-Making Style Recognition Method Using Kinect Technology

“…In fact, machine learning is widely used for classification. Existing research collected physiological characteristics (Darzi et al, 2019;Azari et al, 2020), behavioral performance (Darzi et al, 2019;Leiding et al, 2021), personality characteristics (Darzi et al, 2019;Azari et al, 2020), electronic medical records (Edgcomb et al, 2021) as training data training models, and use the established model to classify indicators such as emotions (Azari et al, 2020), behavioral disorders (Gonzalez, 2020a), depression (Edgcomb et al, 2021) and violent crime risk (Leiding et al, 2021), and achieved valuable research results. In addition, the model established in this study is a supervised learning that uses labeled data for training.…”