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...

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Publicado en:Journal of Loss & Trauma Vol. 29; no. 5; pp. 517 - 544
Autores principales: Szyfer Lipinsky, Aiala, Goldner, Limor, Hadar, Dana
Formato: Artículo
Publicado: Taylor & Francis Ltd 2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        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
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