Decision-Making Modeling for Treating Intimate Partner Violence

治疗亲密伴侣暴力的决策模型

• 批准号: 9756458
• 负责人: Gunnur Karakurt Koyuturk
• 金额: $ 31.34万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-06 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9756458
• 关键词: Address; Affect; Age; Bayesian Network; Cessation of life; Clinical; Cluster Analysis; Communities; Complex; Custom; Data; Data Analyses; Decision Making; Dimensions; Economics; Effectiveness; Ethnic Origin; Evaluation; Evidence based treatment; Factor Analysis; Family; Female; Gender; Goals; Group Therapy; Grouping; Guidelines; Immigration; Individual; Inequality; Injury; Intervention; Investigation; Lead; Literature; Meta-Analysis; Methods; Mining; Modeling; Outcome; Outcomes Research; Participant; Pattern; Play; Population; Psychopathology; Quality of life; Race; Recovery; Reporting; Research; Resources; Role; Series; Severities; Sexual abuse; Site; Social Environment; Societies; Subgroup; Support Groups; Survivors; System; Techniques; Treatment Effectiveness; Treatment outcome; Validation; Victimization; Violence; Woman; Work; age related; anger management; base; data mining; demographics; disparity reduction; effective therapy; emotional abuse; experience; handbook; improved; intimate partner violence; men; model building; offender; optimal treatments; partner violence; perpetrators; physical abuse; recidivism; relationship violence; research study; response; satisfaction; social; socioeconomics; standard care; success; systematic review; tool; treatment group; treatment response

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.

项目摘要 亲密伴侣暴力（IPV）被定义为任何身体，性或情感虐待的电流 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 妇女在其一生中遭受伴侣暴力，其中许多情况导致严重伤害或死亡。 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。性别特异 团体治疗被广泛认为是IPV的标准治疗，但一些参与者 这些群体并没有因为这些治疗而减少暴力。 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，不同的治疗方法可能更有效地减少暴力惯犯。 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。标准IPV治疗并不反映 这种可变性，并没有为所有正在挣扎的人提供平等的恢复机会。 IPV。如果我们能确定哪些亚组人群对治疗的反应相似， 哪种治疗对每个亚组都有最好的结果，我们将能够减少治疗 消除不平等，改善IPV患者的生活质量。这项研究将解决 这个问题有三个目标： 目标1 -我们将对现有证据进行系统回顾和荟萃分析， 描述不同治疗模式的治疗结果。我们将 审查先前研究的数据，以评估暴力程度， 关系满意度这将揭示哪种治疗方法最有效地减少 暴力累犯率。 目标2 -我们将使用数据驱动的方法，系统地调查模式， 以确定对治疗有类似反应的个人亚组。 人口、社会经济、文化和年龄相关因素将在评估期间予以考虑。 亚组识别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种决策工具，帮助他们在以下两种选择之间做出选择： 针对每种情况的循证治疗。目标1和2的结果将用于 特征的选择。这将涉及贝叶斯和回归网络。我们将 将由此产生的决策模型与使用传统方法构建的模型进行比较 功能. 这项研究的结果将减少许多人面临的不平等， 目前只提供了一般性的治疗这一复杂的问题，虽然他们的情况 需要量身定制的解决方案。