个人特征对攻击性和/或恐吓方法的易感性的影响:通过创建受害者模型来量化概率路径
Kay de Wit1,2, Melissa Begeman1, Wouter Noordkamp3
1Department of Human Behaviour and Training, the Netherlands Organization for Applied Sciences (TNO), Soesterberg, the Netherlands.
European journal of psychotraumatology
|December 13, 2023
概括
这项研究量化了年轻女性受害的个体风险因素,开发了一个模型来预测易受攻击性行为 (如亲密伴侣暴力和欺凌) 的易感性.
科学领域:
- 公共卫生 公共卫生
- 犯罪学 犯罪学
- 心理学 心理学 心理学
背景情况:
- 现有的研究确定了导致亲密伴侣暴力 (IPV),欺凌,性虐待和商业性剥削 (CSE) 成为受害者的因素,主要关注女性受害者.
- 量化这些因素对个人的攻击性或恐吓行为的累积影响仍然具有挑战性.
- 之前的研究往往侧重于特定类型的受害者,可能无法捕捉多种风险因素的综合影响.
研究的目的:
- 开发一个全面的风险模型,以量化已知特征对个人易受攻击性/恐吓方法的累积影响.
- 为评估在IPV和性虐待等背景下受害的可能性提供一个工具.
- 为了确定不同类型的受害者的特定风险概况.
主要方法:
- 开发一个贝叶斯信念网络,利用先前研究的数据.
- 该模型捕捉了先前识别的特征对受害风险的多变量贡献.
- 在IPV和CSE的背景下,专注于12-24岁的女性受害者.
主要成果:
- 该模型表明,特定的特征组合显著增加了受害的可能性.
- 受害的可能性可以量化和分类为集群,区分物理,非物理和性攻击/恐吓方法.
- 确定了与不同形式的受害者的脆弱性增加相关的特定风险概况.
结论:
- 该模型是第一个成功量化个人特征对受害风险的累积贡献的模型.
- 这些发现表明,潜在的潜在变量调解了风险因素和整体受害者易感性之间的关系.
- 开发的模型作为一个初始框架,用于识别具有更高风险的个人攻击性/恐吓方法和随后的受害.
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