RFA-CE-23-006 - Rigorous examination of anonymous reporting system data to prevent youth suicide and firearm violence: an applied natural language approach

摘要 匿名和机密报告系统(ARS)在美国广泛实施，超过50% 至少有一个这样的报告系统的学校。ARS和相关的校本举报热线如下 旨在通过消除举报障碍，促进学生分享令人担忧或可疑的行为 比如“告密”的社会成本，支持学生正确识别和报告威胁的自我效能感， 并围绕学校安全制定规范。最初是为应对与学校有关的威胁而开发的 在枪击事件中，ARS的效用如此之大，以至于学生可以报告无数的担忧，包括与精神疾病有关的担忧 健康、欺凌行为、药物使用、武器和亲密伴侣/家庭暴力等。 目前，有20多个州强制要求学生访问某种形式的匿名或机密报告 系统。尽管它们被广泛采用并可供学生使用，但可用的经验数据有限 关于这些系统的运作及其防止校园内枪支和其他暴力伤害的能力 上下文。ARS提供者的轶事和总结报告强调了预防青年伤害的潜力， 但目前对匿名举报系统的深入分析很少。缺乏可用的数据阻碍了 为小费类型编目的标准化，确定可信度的标准，小费管理和 跨ARS和相关系统的解决方案。实施ARS的其他常见挑战包括提高 学生意识和社区认同，采取行动的信息不足，以及 与利益相关者实时通信。这些问题因收到的小费数量和小费的性质而变得更加复杂 数据在形式上通常是叙事的。检查TIP数据的变化--包括TIP内容和因素 前面的提示-可以通知进程对提示进行分类和响应。进一步地，了解学生们 报告以及关于谁可以发现报告系统中的潜在偏见和低效。最后， 虽然大多数报告系统都伴随着学校使用该系统的培训，但没有 我一直在检查培训暴露是否会影响TIP的数量或应答者如何解决TIP 担忧。使用自然语言处理和混合效果回归建模，我们的研究将利用 包含17,000多条提示的大型半结构化数据集在5年内提交给全州范围的ARS，以便 描述报告的提示类型、影响学生提交的提示和内容的因素，以及 接受培训是否会影响小费的行为和内容。我们将向学校展示我们的发现--以及 报告-用于指导翻译的系统利益相关者，以确定可操作的战略和最佳实践 完善和规范报告制度。使用二级数据为初级预防工作提供信息 与融资机会的融资选项A保持一致。

ABSTRACT Anonymous and confidential reporting systems (ARS) are widely implemented in the U.S., with more than 50% of schools having access to at least one such reporting system. ARS and related school-based tip lines are designed to facilitate student sharing of concerning or suspicious behaviors by eliminating barriers to reporting, such as the social cost of ‘tattling’, supporting students’ self-efficacy to correctly identify and report a threat, and creating norms around school safety. Originally developed in response to threats related to school shootings, the utility of ARS is such that students can report myriad concerns, including those related to mental health, bullying behavior, substance use, weapons, and intimate partner/domestic violence, among others. Currently, more than 20 states mandate student access to some form of anonymous or confidential reporting system. Despite their widespread adoption and availability to students, there is limited empirical data available regarding the functioning of these systems and their ability to prevent firearm and other violent injury in school contexts. Anecdotal and summary reports by ARS providers highlight the potential for preventing youth injury, but few in-depth analyses of anonymous reporting systems currently exist. The lack of available data inhibits standardization for cataloging the type of tips, criteria for determining credibility, and tip management and resolution across ARS and related systems. Other common challenges to ARS implementation include raising student awareness and community buy-in, insufficient information to act, and coordinating information among stakeholders in real time. These problems are compounded by the volume of tips received and the nature of tip data which is typically narrative in form. Examining variation in tip data - including the tip content and factors preceding tips - can inform processes to triaging and responding to tips. Further, understanding what students report and about whom can uncover both potential bias and inefficiencies in reporting systems. Finally, although most reporting systems are accompanied by school training in the use of the system, there has not been an examination of whether training exposure affects the number of tips or how responders resolve tip concerns. Using natural language processing and mixed effects regression modeling, our study will leverage a large, semistructured data set of more than 17,000 tips submitted to a statewide ARS over 5 years in order to characterize the types of tips reported, what factors influence student tip submissions and content, and whether exposure to training influences tip behavior and content. We will present our findings to school- and reporting-system stakeholders for guided translation to identify actionable strategies and best practices for improving and standardizing reporting systems. The use of secondary data to inform primary prevention efforts is aligned with Funding Option A of the funding opportunity.