Longitudinal predictors of children's self-blame appraisals among military families reported for family violence

针对有家庭暴力史的军人家庭,纵向研究预测了儿童的自责程度。

阅读:1

Abstract

BACKGROUND: Although children's self-blame appraisals are recognized as important sequelae of child victimization that contribute to subsequent adjustment problems, little is known about the factors that predict their development and longitudinal course. OBJECTIVE: The current study examines the stability and longitudinal predictors of children's self-blame appraisals among a sample of children reported for family violence. PARTICIPANTS AND SETTING: Children (N = 195; 63 % female) aged 7 to 17 years (M(age) = 12.17) were recruited as part of a longitudinal assessment of families referred to the United States Navy's Family Advocacy Program due to allegations of child physical abuse, sexual abuse, or intimate partner violence. METHODS: Children completed assessments on self-blame at 3 time points (baseline, 9-12 months, and 18-24 months) and baseline measures of their victimization experience, caregiver-child conflict, and depression. RESULTS: In univariate analyses, victimization that involved injury (r = 0.29, p < .001), the number of perpetrators (r = 0.23, p = .001), the number of victimization types (r = 0.32, p < .001), caregiver-child conflict (r = 0.36, p < .001), and depression (r = 0.39, p < .001) were each positively associated with baseline self-blame. When examined in a single longitudinal multilevel model, results indicated only caregiver-child conflict (b = 0.08, p = .007) and baseline depression (b = 0.06, p = .013) predicted increases in self-blame. CONCLUSION: Findings suggest clinicians and researchers may consider assessment of victimization characteristics, caregiver-child relationships, and depression symptoms to identify children most at risk for developing self-blame appraisals.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。