Epidemiologic and Machine Learning Approaches to Frame Suicide Prevention Strategies Among Juvenile Justice Youth - 2021

流行病学和机器学习方法在少年司法青年中制定自杀预防策略 - 2021

• 批准号: 10647716
• 负责人: Donna Ruch
• 金额: $ 12.45万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-16 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647716
• 关键词: Address; Adolescent; Applications Grants; Attention; Back; Cause of Death; Characteristics; Clinical Trials; Communities; Complex; Environment; Environmental Risk Factor; Epidemiology; Face; Funding; Future; Goals; Health Services; Healthcare; Implementation readiness; Imprisonment; Individual; Intake; Intervention; Justice; Knowledge; Link; Longitudinal cohort study; Machine Learning; Mental Health; Mental Health Services; Modeling; National Institute of Mental Health; Persons; Policies; Population; Prevention; Prevention Research; Prevention strategy; Procedures; Process; Public Health; Qualitative Research; Recommendation; Research; Research Methodology; Research Priority; Resources; Risk; Services; Structure; Suicide; Suicide prevention; System; Techniques; Time; Training; Training Support; Translating; Underserved Population; United States; Victimization; Violence; Vulnerable Populations; Youth; advanced analytics; aged; career; contextual factors; court; data integration; effectiveness evaluation; evidence base; health care service; high risk; implementation barriers; implementation framework; implementation science; improved; innovation; innovative technologies; insight; juvenile justice system; machine learning model; novel; preventive intervention; reducing suicide; risk prediction; risk prediction model; segregation; skill acquisition; skills; suicidal; suicidal behavior; suicidal risk; suicide rate

PROJECT TITLE Epidemiologic and Machine Learning Approaches to Frame Suicide Prevention Strategies Among Juvenile Justice Youth PROJECT SUMMARY / ABSTRACT Suicide is the second leading cause of death among youth aged 10-24 years in the U.S. One population found to have higher rates of suicidal behavior is youth incarcerated in the juvenile justice system. While progress has been made to reduce suicide for youth within juvenile correctional facilities, minimal consideration has been given to the risk for suicide in youth after incarceration. Previously incarcerated youth face numerous challenges reintegrating back into the community which can increase their risk for suicidal behavior. Estimates further suggest that 60% to 80% of youth involved in the justice system have significant mental health issues, and time spent in the system can exacerbate these conditions. The unmet need for mental health services by youth involved with the justice system is also a serious problem. Despite the recognized risk in this vulnerable population, evidence-based suicide prevention strategies are not integrated as part of routine reentry services for youth released from confinement. Even less is known about successful approaches to implement these strategies in juvenile justice systems. To address this gap, the proposed study uses innovative machine learning techniques to develop a risk prediction model incorporating youth characteristics and contextual factors associated with confinement (violence, victimization, segregation /isolation practices, health care services) to more accurately assess suicide risk in youth following incarcerated. Guided by these findings and a structured implementation science framework, this proposal will also conduct a pre-intervention assessment with key stakeholders to validate the utility of the machine learning model to inform intervention selection. Consideration will be given to potential facilitators and barriers to integrating the model into practice, and when, how, and where to intervene in the juvenile justice process. Achieving the aims of this proposed study will provide targeted intervention recommendations for suicide prevention among at-risk youth in the juvenile justice system. This proposal will also support the training of Dr. Ruch, who is devoted to a research career to reduce suicide in youth involved with justice system. Dr. Ruch’s training plan includes: (1) acquiring skills in machine learning and forecast modeling techniques to more accurately identify suicide risk and inform targeted preventions for youth in the justice system (2) enhancing knowledge of suicide prevention interventions, including health service systems to understand how health care practices and policies may facilitate or impede intervention for youth involved with the justice system and (3) strengthening skills in implementation science and advanced qualitative research methods to bridge the gap between research and practice. This line of inquiry will further the field of youth suicide research by introducing innovative technological approaches for suicide prevention in a significantly high-risk and underserved population, while also generating new insights about the distinct implementation challenges in the potentially resource constrained juvenile justice system.

项目名称 流行病学和机器学习方法在青少年中制定自杀预防策略 正义青年 项目总结/摘要 自杀是美国10-24岁青少年死亡的第二大原因。 被发现有较高自杀行为率的是被关押在少年司法系统中的青少年。而 在减少青少年管教设施内的青少年自杀方面取得了进展， 已考虑到青年人在监禁后自杀的风险。曾被监禁 青年面临着许多重返社会的挑战，这可能增加他们的风险， 自杀行为估计还表明，60%至80%的司法系统所涉青年 严重的心理健康问题，以及在系统中花费的时间可能会加剧这些情况。未满足的 司法系统所涉青年对心理健康服务的需求也是一个严重问题。尽管 这一脆弱人群中公认的风险，基于证据的自杀预防策略不是 作为解除监禁青年重返社会的例行服务的一部分。更少有人知道 在少年司法系统中实施这些战略的成功方法。为了弥补这一差距， 拟议的研究使用创新的机器学习技术来开发风险预测模型 结合青年特点和与监禁有关的背景因素（暴力， 受害，隔离/隔离做法，医疗保健服务），以更准确地评估自杀风险， 年轻人被监禁后。在这些调查结果和结构化的实施科学框架的指导下， 该提案还将与关键利益相关者一起进行干预前评估，以验证 机器学习模型为干预选择提供信息。将考虑潜在的 促进者和障碍，将模式纳入实践，以及何时，如何，以及在何处进行干预， 少年司法程序。实现这项拟议研究的目标将提供有针对性的干预措施 关于预防少年司法系统中高危青年自杀的建议。这项建议 还将支持鲁赫博士的培训，鲁赫博士致力于减少青年自杀的研究事业 与司法系统有关。Ruch博士的培训计划包括：（1）获得机器学习技能， 预测建模技术，以更准确地识别自杀风险，并为有针对性的预防提供信息， 司法系统中的青年（2）提高对自杀预防干预措施的认识，包括健康 服务系统，以了解卫生保健做法和政策如何促进或阻碍干预 为参与司法系统的青年提供培训;（3）加强执行科学和 先进的定性研究方法，以弥合研究与实践之间的差距。这一调查线索 将通过引入创新的自杀技术方法， 在高风险和服务不足的人群中进行预防，同时也产生了关于以下方面的新见解： 在资源可能有限的少年司法系统中，存在着明显的执行挑战。