Promoting prosocial bystander behavior in intoxicated men: Evaluation of RealConsent2.0

性暴力在美国是一个重大的公共卫生问题，特别是在18-24岁的人群中 老年(大学和非大学)人口。18至24岁的女性强奸率和性侵率最高 受害情况(女大学生为6.1‰；非大学生女生为7.6‰) 同龄男性和其他年龄段的女性。尽管有这些令人震惊的统计数据，疾控中心报告说 只有一个程序(“RealConsend”)被严格证明在增加亲社会的两方面都是有效的 大学生的旁观者行为与预防性侵行为。因此，在Sv中有一个很大的差距 预防规划。尤其重要的是，RealConsent和其他旁观者 干预方案及其各自的评价存在严重的弱点和局限性， 减少对SV费率的总体影响。这些问题包括：(1)过分强调旁观者的态度或意图 相对于实际行为，(2)依赖于自我报告的衡量标准，当行为是 评估，(3)没有解决酒精对旁观者决策和行为的近期影响 SV面临风险的情况，以及(4)泛化能力差，因为最少关注的是没有 上大学。干预研究必须直接解决这些限制，以缩小这一关键差距。 这项提案的科学前提是，通过将特定于酒精使用的新内容整合到 在SV介入RealContent的背景下，我们可以更有效地促进亲社会和有效的旁观者 行为--即使在醉酒的旁观者中也是如此。我们的目标是首先检查近端饮酒对 虚拟旁观者行为，然后测试修改后的RealConent程序的有效性(即， RealConsent2.0)。这些目标将通过(1)为RealConsen2.0开发新的计划内容来实现 是由酒精近视理论和旁观者两个理论视角的整合而得到的 决策模型，(2)在两个不同地理位置实施随机对照试验 地区(东南部和中西部)有足够的力量，以社区为基础的样本N=840名年轻人 男性，(3)比较RealConsen1.0和2.0对以下人群可观察到的旁观者行为的相对有效性 干预后1个月在虚拟现实环境中醉酒和清醒的旁观者，以及(4) RealConsen1.0和2.0对自述旁观者行为的有效性比较 近端酒精使用和6个月和12个月后行为背景中酒精的存在 干预。拟议项目最重要的贡献将是提供第一个证据 如何促进饮酒男性的亲社会旁观者行为，促进未来发展， 评估和传播旁观者干预方案。通过这样做，该项目将提供 为现有的旁观者预防规划提供必要的经验基础，以翻译这些发现 直接进入现实世界的应用程序，这最终将对SV产生重大影响。

Sexual violence (SV) is a significant public health problem in the United States, particularly among 18-24 year old (college and non-college) populations. Females 18 to 24 have the highest rate of rape and sexual assault victimization (6.1 per 1,000 for female college students; 7.6 per 1,000 for female nonstudents) compared to males of the same age and females of other age groups. Despite these alarming statistics, the CDC reports that there is only one program (“RealConsent”) rigorously shown to be effective in both increasing prosocial bystander behaviors and preventing SV perpetration among college students. Thus, there is a major gap in SV prevention programming. Of particular importance is the fact that RealConsent and other bystander intervention programs and their respective evaluations suffer from critical weaknesses and limitations that reduce overall impact on SV rates. These include: (1) an overemphasis on bystander attitudes or intentions versus actual behavior, (2) measures that rely upon self-report and are void of context when behavior is assessed, (3) not addressing the proximal effect of alcohol on bystander decision making and behavior in situations at-risk for SV, and (4) poor generalizability given the minimal focus on young adults who do not attend college. Intervention research must directly address these limitations in order to close this critical gap. The scientific premise of this proposal is that by integrating new content specific to alcohol use within the context of SV intervention into RealConsent, we can more effectively facilitate prosocial and effective bystander behavior – even among intoxicated bystanders. We aim to first examine the effects of proximal alcohol use on virtual bystander behaviors and then test the efficacy of the revised RealConsent program (i.e., RealConsent2.0). These goals will be achieved by (1) developing new program content for RealConsent2.0 that is informed by the integration of two theoretical perspectives – Alcohol Myopia Theory and the Bystander Decision-Making Model, (2) implementing a randomized controlled trial (RCT) in two distinct geographic areas (the Southeast and Midwest) with a sufficiently-powered, community-based sample of N=840 young men, (3) comparing the relative efficacy of RealConsent1.0 and 2.0 on observable bystander behavior among intoxicated and sober bystanders in a virtual reality environment 1-month post-intervention, and (4) comparing the efficacy of RealConsent1.0 and 2.0 on self-reported bystander behavior as a function of proximal alcohol use and the presence of alcohol within the behavioral context at 6- and 12-months post- intervention. The most significant contribution of the proposed project will be to provide the first evidence of how to promote prosocial bystander behavior in men who consume alcohol and improve future development, evaluation, and dissemination of bystander intervention programs. In doing so, this project will provide the necessary empirical foundation for existing bystander prevention programming to translate these findings directly into real-world applications that will ultimately have a significant impact on SV.