以创伤为重点的青少年约会暴力预防筛查方法。

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Prevention Science Pub Date : 2025-01-01 Epub Date: 2025-01-09 DOI:10.1007/s11121-025-01772-4
Joseph R Cohen, Jae Wan Choi, Jaclyn S Fishbach, Jeff R Temple
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引用次数: 0

摘要

制定准确和公平的筛查方案可以使青少年约会暴力(TDV)预防规划更有针对性、更高效和更有效。目前的TDV筛查方案表现不佳,很少得到实施,但最近的研究和政策强调了利用更多以创伤为重点的筛查措施来改善预防结果的重要性。作为回应,本研究考察了哪些逆境(即家庭暴力指数)、以创伤为重点的风险因素(即威胁和奖励偏见)和优势(即社会支持和种族/民族认同)最适合归类为tdv实施的生理和心理形式的并发和潜在风险。参与者包括584名12-18岁的青少年(MAge = 14.43;SD = 1.22),均匀分布在性别(48.9%女性)、种族(35%非裔美国人;38.5%白人)和种族(40%西班牙裔)。基线时完成的调查和1年随访使用循证医学(EBM)分析方案进行分析(即逻辑回归、曲线下面积;(AUC)、诊断似然比(DLR)、校准曲线),并与机器学习模型进行比较。结果显示,敌意是并发和预期物理tdv发生的最佳分类风险(auc为0.70;DLRs bbb2.0)。此外,家庭暴力(DV)暴露最能预测心理上的tdv实施(AUC bb0 0.70;dl> 3.0)。这两个指数都经过了良好的校准(即,Spiegelhalter的Z统计数据不显著),并且在统计上是公平的。机器学习模型增加了最小的增量有效性。结果表明,优先考虑敌意和dv暴露对于准确、公平和可行地筛查生理和心理形式的tdv犯罪的重要性。将这些发现整合到现有的预防方案中,可以产生更有针对性的方法来减少tdv的发生。
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A Trauma-Focused Screening Approach for Teen Dating Violence Prevention.

Developing accurate and equitable screening protocols can lead to more targeted, efficient, and effective, teen dating violence (TDV) prevention programming. Current TDV screening protocols perform poorly and are rarely implemented, but recent research and policy emphasizes the importance of leveraging more trauma-focused screening measures for improved prevention outcomes. In response, the present study examined which adversities (i.e., indices of family violence), trauma-focused risk factors (i.e., threat and reward biases) and strengths (i.e., social support and racial/ethnic identity) best classified concurrent and prospective risk for physical and psychological forms of TDV-perpetration. Participants included 584 adolescents aged 12-18 years (MAge = 14.43; SD = 1.22), evenly distributed across gender (48.9% female), race (35% African American; 38.5% White) and ethnicity (40% Hispanic). Surveys completed at baseline and 1-year follow-up were analyzed using an evidence-based medicine (EBM) analytic protocol (i.e., logistic regression, area-under-the-curve; (AUC), diagnostic likelihood ratios (DLR), calibration curves) and compared to machine learning models. Results revealed hostility best classified risk for concurrent and prospective physical TDV-perpetration (AUCs > 0.70; DLRs > 2.0). Additionally, domestic violence (DV) exposure best forecasted prospective psychological TDV-perpetration (AUC > 0.70; DLR > 3.0). Both indices were well-calibrated (i.e., non-significant Spiegelhalter's Z statistics) and statistically fair. Machine learning models added minimal incremental validity. Results demonstrate the importance of prioritizing hostility and DV-exposure for accurate, equitable, and feasible screening for physical and psychological forms of TDV-perpetration, respectively. Integrating these findings into existing prevention protocols can lead to a more targeted approach to reducing TDV-perpetration.

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来源期刊
Prevention Science
Prevention Science PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.50
自引率
11.40%
发文量
128
期刊介绍: Prevention Science is the official publication of the Society for Prevention Research. The Journal serves as an interdisciplinary forum designed to disseminate new developments in the theory, research and practice of prevention. Prevention sciences encompassing etiology, epidemiology and intervention are represented through peer-reviewed original research articles on a variety of health and social problems, including but not limited to substance abuse, mental health, HIV/AIDS, violence, accidents, teenage pregnancy, suicide, delinquency, STD''s, obesity, diet/nutrition, exercise, and chronic illness. The journal also publishes literature reviews, theoretical articles, meta-analyses, systematic reviews, brief reports, replication studies, and papers concerning new developments in methodology.
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