Predicting Psychopathology in Jewish Ultra-Orthodox IPV Survivors: A Machine Learning Approach.
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. Co...
| Publicado en: | Journal of Loss & Trauma Vol. 29; no. 5; pp. 517 - 544 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=178068595&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 178068595 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 15325024 HZH jtl: Journal of Loss & Trauma issn: 15325024 maglogo: N pubinfo: dt: 2024 vid: 29 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 178068595 10.1080/15325024.2023.2280613 ppf: 517 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Predicting Psychopathology in Jewish Ultra-Orthodox IPV Survivors: A Machine Learning Approach. aug: au: Szyfer Lipinsky, Aiala Goldner, Limor Hadar, Dana affil: School of Creative Arts Therapies, Emili Sagol Creative Arts Therapies Research Center, Faculty of Social Welfare and Health Sciences, University of Haifa, Israel su: Post-traumatic stress disorder Intimate partner violence Torture victims Violence Psychology of women Social norms Help-seeking behavior Psychological adaptation Religion Quality of life Sociodemographic factors Health promotion Psychosocial factors Well-being Social stigma Risk assessment Random forest algorithms Prediction models Ultra-Orthodox Jews Machine learning Regression analysis sug: subj: Post-traumatic stress disorder Intimate partner violence Torture victims Violence Psychology of women Social norms Help-seeking behavior Psychological adaptation Religion Quality of life Sociodemographic factors Health promotion Psychosocial factors Well-being Social stigma Risk assessment Random forest algorithms Prediction models Ultra-Orthodox Jews Machine learning Regression analysis keyword: Jewish ultra-orthodox self-blame self-stigma wellbeing Jewish ultra-orthodox self-blame self-stigma wellbeing ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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