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.

摘要 匿名和机密报告系统（ARS）在美国广泛实施，超过50% 学校至少有一个这样的报告系统。ARS和相关的学校举报热线是 旨在通过消除报告障碍，促进学生分享有关或可疑行为， 例如“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， 建立学校安全的规范。最初是为了应对与学校有关的威胁而开发的 枪击事件，ARS的效用是这样的，学生可以报告无数的问题，包括那些与精神 健康、欺凌行为、物质使用、武器和亲密伴侣/家庭暴力等。 目前，超过20个州要求学生获得某种形式的匿名或机密报告 系统尽管它们被学生广泛采用和使用，但可用的经验数据有限 关于这些系统的运作及其在学校防止火器和其他暴力伤害的能力， contexts. ARS提供者的轶事和总结报告强调了预防青少年伤害的潜力， 但目前对匿名举报制度的深入分析很少。由于缺乏可用数据， * 对举报类型进行分类的标准化、确定可信度的标准和举报管理， 解决ARS和相关系统的问题。实施农业可再生能源系统面临的其他共同挑战包括： 学生的认识和社区的购买，采取行动的信息不足，以及协调信息之间 真实的时间。这些问题由于收到的小费的数量和小费的性质而变得更加复杂。 通常以叙述形式出现的数据。检查小费数据的变化-包括小费内容和因素 在前提示-可以通知处理以分类和响应提示。此外，了解学生 报告和关于谁可以发现潜在的偏见和报告系统的效率低下。最后， 虽然大多数报告系统都附有使用该系统的学校培训， 一直在检查培训暴露是否会影响提示的数量或应答者如何解决提示 性问题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 超过17，000个提示的大型半结构化数据集在5年内提交给全州ARS，以便 描述报告的提示类型，哪些因素影响学生提示提交和内容，以及 接受培训是否会影响小费行为和内容。我们将把我们的发现提交给学校， 报告系统利益相关者进行指导翻译，以确定可操作的战略和最佳实践， 改进和规范报告制度。利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。