Predicting Psychopathology in Jewish Ultra-Orthodox IPV Survivors: A Machine Learning Approach

Abstract

The nature of the abuse, cultural and religious values, trauma-related cognitions, and recovery actions are considered factors that shape intimate partner violence (IPV) survivors’ recovery and pathology. However, less is known about their specific impact on women’s psychopathology and wellbeing. Concomitantly, there is scant information about IPV survivors from collectivistic societies such as the Israeli Jewish Ultra-orthodox (JUO) community. The present study was designed to identify predictors of post-traumatic stress (PTSD) symptoms and wellbeing in women from the JUO community who have experienced IPV. Women (N = 261) provided information about their demographics, the nature of the violence, attitudes with respect to cultural and religious norms that normalize violence, trauma-related cognitions, the coping constructs of disengagement, faith, and engaging in help-seeking and recovery actions, and the PTSD symptoms that affect their wellbeing. A Random Forest machine learning (ML) algorithm was used to identify the strongest predictors of psychopathology and wellbeing. Regression trees were developed to identify individuals at greater risk of PTSD symptoms but also of greater wellbeing. Higher self-stigma and the perception of an unsafe world were associated with PTSD symptoms, whereas lower self-stigma, greater faith, and engagement in steps toward recovery were associated with greater wellbeing. These findings highlight the importance of treating women’s self-stigma and perceptions of an unsafe world while also encouraging faith and active engagement in recovery to promote survivors’ wellbeing and lessen their PTSD symptoms.

Keywords:

• Intimate partner violence
• self-stigma
• self-blame
• Jewish ultra-orthodox
• wellbeing

Additional information

Notes on contributors

Aiala Szyfer Lipinsky

Aiala Szyfer Lipinsky is an accredited Art therapist, supervisor, and Ph.D. candidate at the School of Creative Arts Therapies, the Emili Sagol research center, and the laboratory for at-risk children and adolescents at the University of Haifa. She specializes in treating and studying child sexual abuse and domestic violence in the ultra-Orthodox sector.

Limor Goldner

Limor Goldner (Ph.D.) is an associate professor at the Graduate School of Creative Arts Therapies in the Faculty of Welfare and Health Sciences at the University of Haifa. She serves as the head of the School of Creative Arts Therapies, the director of the Emili Sagol research center, and the laboratory for at-risk children and adolescents. She concentrates on studying children’s emotional and sexual abuse and the recovery process of women who experience gender-based violence and their manifestations in visual art. In the later issues, she published about 60 scientific papers and chapters.

Dana Hadar

Dana Hadar (M.A.) is a data scientist with solid statistics, programming, and visualization, implementing machine learning algorithms and traditional statistics to solve real-world problems using R, Python, SAS, SQL, and Tableau. She Works at the Emili Sagol research center, the Department of Physiotherapy and Psychotherapy research lab at the University of Haifa, converting research questions into feature engineering, visualization, and modeling using Python pandas and scikit-learn, R, SAS, and Mplus.