Decision-Making Modeling for Treating Intimate Partner Violence

项目摘要 亲密伴侣暴力(IPV)被定义为对当前伴侣的任何身体、性或情感虐待 或前亲密伴侣。大约四分之一的女性会经历某种形式的严重 伴侣在一生中遭受暴力，其中许多情况会导致重伤或死亡。 虽然男性也可能成为IPV的受害者，但大多数案件涉及女性受害者。特定性别 团体疗法被广泛认为是治疗IPV的标准疗法，但一些参与者 这些群体并没有因为这些治疗而经历暴力的减少。 关于标准治疗有效性的报告以及研究结果表明， 在某些情况下，不同的治疗方法在减少暴力再次犯罪方面可能更有效。 许多因素影响参与者对治疗的反应。这些因素包括 人口统计、暴力类型和治疗提供情况。标准的IPV治疗不能反映 这种可变性，并没有为所有正在与之作斗争的人提供平等的复苏机会 灭活脊髓灰质炎病毒疫苗。如果我们能确定人群中哪些亚群对治疗的反应相似，以及 哪些治疗方法对每个亚组的效果最好，我们将能够减少治疗 不平等和改善患有肺炎的人的生活质量。这项研究将解决 这个问题有三个目的： 目标1-我们将对现有证据进行系统审查和荟萃分析，以 根据不同的治疗模式确定治疗结果。我们会 检查来自先前研究的数据，以评估暴力程度和 关系满足感。这将揭示出哪种治疗方法在减少 每个小组的暴力累犯率。 目标2-我们将使用数据驱动的方法来系统地研究 暴力，以确定对治疗反应相似的个人亚群。 与人口、社会经济、文化和年龄有关的因素将在 子组标识。这将涉及潜在的阶级分析。 目标3-我们将为临床医生开发一个决策工具，帮助他们在 针对每种情况的循证治疗。目标1和目标2的结果将用于 特征的选择。这将涉及贝叶斯网络和回归网络。我们会 将生成的决策模型与使用传统的 功能。 这项研究的结果将减少许多个人面临的不平等 目前只提供了对这个复杂问题的通用治疗，尽管他们的情况 呼吁提供量身定制的解决方案。

Project Summary Intimate partner violence (IPV) is defined as any physical, sexual, or emotional abuse of a current or former intimate partner. Approximately one in four women will experience some form of severe partner violence during their lifetime, and many of these situations result in serious injury or death. Although men can also be victims of IPV, most cases involve female victimization. Gender specific group therapy is widely considered as the standard treatment for IPV, but some participants of these groups do not experience a decrease in violence in response to these treatments. Reports on the effectiveness of standard treatments, as well as research findings suggest that different treatments may be more effective in reducing violence recidivism in certain situations. Many factors influence how participants respond to treatment. These factors include demographics, types of violence, and treatment delivery. Standard IPV treatment does not reflect this variability, and does not provide equal opportunity for recovery to all who are struggling with IPV. If we can determine which subgroups of the population respond similarly to treatment, and which treatments lead to the best outcomes for each subgroup, we will be able to reduce treatment inequalities and improve the quality of life for people suffering from IPV. This study will address this problem in three aims: Aim 1 – We will conduct a systematic review and meta-analysis of existing evidence to characterize treatment outcomes in response to different treatment models. We will examine data from pre-existing research studies to assess levels of violence and relationship satisfaction. This will reveal which treatment is most effective in reducing violence recidivism for each subgroup. Aim 2 – We will use a data-driven approach to systematically investigate patterns of violence to identify subgroups of individuals who respond similarly to treatment. Demographic, socioeconomic, cultural, and age related factors will be considered during subgroup identification. This will involve latent class analysis. Aim 3 – We will develop a decision making tool for clinicians to help them choose between evidence based treatments for each situation. Results from Aims 1 and 2 will be used in the selection of features. This will involve Bayesian and regression networks. We will compare the resulting decision making models to models that are built using traditional features. The outcome of this research will reduce the inequality faced by many individuals who are currently only offered generic treatment for this complex problem, although their circumstances call for tailored solutions.