Young adults experiencing homelessness (YAEH) are at higher risk for intimate partner violence (IPV) victimization than their housed peers. This is often due to their increased vulnerability to abuse and victimization before and during homelessness, which can result in a cycle of violence in which YAEH also perpetrates IPV. Identifying and addressing factors contributing to IPV perpetration at an early stage can reduce the risk of IPV. Yet to date, research examining YAEH’s IPV perpetration is scarce and has largely employed conventional statistical approaches that are limited in modeling this complex phenomenon. To address these gaps, this study used an interpretable machine learning approach to answer the research question: What are the most salient predictors of IPV perpetration among a large sample of YAEH in seven U.S. cities? Participants ( N = 1,426) on average were 21 years old ( SD = 2.09) and were largely cisgender males (59%) and racially/ethnically diverse (81% were from historically excluded racial/ethnic groups; i.e., African American, Latino/a, American Indian, Asian or Pacific Islander, and mixed race/ethnicity). Over one-quarter (26%) reported IPV victimization, and 20% reported IPV perpetration while homeless. Experiencing IPV victimization while homeless was the most important factor in predicting IPV perpetration. An additional 11 predictors (e.g., faced frequent discrimination) were positively associated with IPV perpetration, whereas 8 predictors (e.g., reported higher scores of mindfulness) were negatively associated. These findings underscore the importance of developing and implementing effective interventions with YAEH that can prevent IPV, particularly those that recognize the positive association between victimization and perpetration experiences.

中文翻译：

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使用可解释的机器学习预测美国七个城市无家可归的年轻人的亲密伴侣暴力行为


无家可归的年轻人 (YAEH) 遭受亲密伴侣暴力 (IPV) 侵害的风险高于被安置的同龄人。这通常是因为他们在无家可归之前和无家可归期间更容易受到虐待和受害，这可能导致暴力循环，其中 YAEH 也实施 IPV。尽早识别并解决导致 IPV 发生的因素可以降低 IPV 的风险。然而迄今为止，对 YAEH IPV 实施情况的研究还很少，并且主要采用传统的统计方法，而这些方法在模拟这种复杂现象方面受到限制。为了解决这些差距，本研究使用可解释的机器学习方法来回答研究问题：在美国七个城市的大量 YAEH 样本中，IPV 实施的最显着预测因素是什么？参与者 ( N = 1,426) 平均年龄为 21 岁 ( SD = 2.09)，大部分是顺性别男性 (59%)，并且具有不同的种族/民族（81% 来自历史上被排除的种族/族裔群体；即非裔美国人、拉丁裔/ a、美洲印第安人、亚洲或太平洋岛民以及混血/族裔）。超过四分之一 (26%) 的人报告称受到 IPV 伤害，20% 的人报告称在无家可归时遭受过 IPV 犯罪。无家可归时经历 IPV 受害是预测 IPV 犯罪的最重要因素。另外 11 个预测因素（例如，经常面临歧视）与 IPV 实施呈正相关，而 8 个预测因素（例如，报告正念得分较高）呈负相关。 这些发现强调了制定和实施有效的 YAEH 干预措施以预防 IPV 的重要性，特别是那些认识到受害与犯罪经历之间存在正相关关系的干预措施。