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

RFA-CE-23-006 - 严格检查匿名报告系统数据以防止青少年自杀和枪支暴力：应用自然语言方法

• 批准号: 10786629
• 负责人: Justin Edward Heinze
• 金额: $ 35万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10786629
• 关键词: RFA; CE; 23; 006; Rigorous

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.

抽象的 匿名和机密报告系统（ARS）在美国广泛实施，超过50％ 在至少一个这样的报告系统的学校中。 AR和相关学校的小费线是 旨在通过消除报告障碍， 例如“纹身”的社会成本，支持学生的自我效能感，以正确识别和报告威胁， 并围绕学校安全制定规范。最初是为了应对与学校有关的威胁而开发的 枪击事件，ARS的效用使学生可以报告无数的问题，包括与心理有关的问题 健康，欺凌行为，使用物质，武器和亲密伴侣/家庭暴力等。 目前，超过20个州要求学生访问某种形式的匿名或机密报告 系统。尽管对学生的宽度和可用性宽度，但可用的经验数据有限 关注这些系统的功能及其防止枪支和其他暴力伤害的能力 上下文。 ARS提供商的轶事和摘要报告强调了预防青年伤害的潜力， 但是目前对匿名报告系统的深入分析很少。缺乏可用数据抑制 针对提示类型的标准化，确定信誉的标准以及提示管理的标准和 跨AR和相关系统的分辨率。 ARS实施的其他常见挑战包括提高 学生意识和社区买入，不足的信息以及协调的信息 实时利益相关者。这些问题被收到的小费的数量和小费的性质加重了 数据通常形式为叙述。检查小费数据的变化 - 包括小费内容和因素 先前的提示 - 可以为过程提供分类和响应技巧的信息。此外，了解学生 报告以及谁可以在报告系统中发现潜在的偏见和效率低下。最后， 尽管大多数报告系统都伴随着使用该系统的学校培训，但尚未 是检查训练曝光是否会影响小费的数量或响应者如何解决小费 关注。使用自然语言处理和混合效应回归建模，我们的研究将利用 大型半结构化数据集，其中17,000多个提示在5年内提交给全州AR的提示 描述报告的提示类型，哪些因素影响学生的提示和内容，以及 接触培训是否会影响小费的行为和内容。我们将向学校和 报告系统利益相关者用于指导翻译，以确定可行的策略和最佳实践 改进和标准化报告系统。使用二级数据来通知主要预防工作 与资金选项A保持一致。