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判别函数分析:对被监禁的农村女性中的毒品/暴力受害类型进行分类

Discriminant Function Analyses: Classifying Drugs/Violence Victimization Typologies Among Incarcerated Rural Women.

作者信息

Victor Grant A, Staton Michele

机构信息

Wayne State University, Detroit, MI, USA.

University of Kentucky, Lexington, USA.

出版信息

J Interpers Violence. 2022 Jan;37(1-2):889-911. doi: 10.1177/0886260520913644. Epub 2020 Apr 23.

Abstract

This study examined the relationship between drug use and violence victimization among incarcerated women in Appalachian Kentucky. The purpose of this study was to test the utility of Goldstein's tripartite conceptual framework among rural incarcerated women, by examining whether distinct drugs/violence nexus groups could be classified based on psychopharmacological, economic-compulsive, and systemic factors. This study used secondary data from a National Institute on Drug Abuse (NIDA)-funded grant focused on risk reduction among high-risk incarcerated women in Appalachia ( = 400). Predicted drugs/violence groups were developed using a series of discriminant function analyses. The data yielded three statistically significant discriminant models. Findings of the classified groupings indicated support for three distinct drugs/violence victimization subgroups. The psychopharmacological group showed the greatest prevalence ( = 181; Wilks's λ = .389, = 3.94, < .001), followed by the economic-compulsive group ( = 77; Wilks's λ = .584, = 11.86, < .001) and systemic group ( = 55) significant (Wilks's λ = .994, = 2.247, < .035). To date, this is the first study to report a relationship between systemic violence victimization among rural communities. These findings could offer novel considerations for theory development and implications for clinical practice regarding the drug-related risks for violence victimization among rural incarcerated women.

摘要

本研究调查了肯塔基州阿巴拉契亚地区被监禁女性的药物使用与暴力受害之间的关系。本研究的目的是通过检验是否可以根据心理药理学、经济强迫性和系统性因素对不同的毒品/暴力关联群体进行分类,来测试戈尔茨坦三方概念框架在农村被监禁女性中的实用性。本研究使用了美国国立药物滥用研究所(NIDA)资助的一项拨款中的二手数据,该拨款专注于阿巴拉契亚地区高危被监禁女性的风险降低( = 400)。使用一系列判别函数分析来建立预测的毒品/暴力群体。数据产生了三个具有统计学意义的判别模型。分类分组的结果表明支持三个不同的毒品/暴力受害亚组。心理药理学组的患病率最高( = 181;威尔克斯的λ = .389, = 3.94, < .001),其次是经济强迫性组( = 77;威尔克斯的λ = .584, = 11.86, < .001)和系统性组( = 55)显著(威尔克斯的λ = .994, = 2.247, < .035)。迄今为止,这是第一项报告农村社区系统性暴力受害之间关系的研究。这些发现可为理论发展提供新的思考,并为农村被监禁女性暴力受害的药物相关风险的临床实践提供启示。

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