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制定青少年自杀预防策略 正义青年 项目概要/摘要 自杀是美国 10-24 岁青少年的第二大死因 人口 研究发现，被监禁在少年司法系统中的青少年自杀行为率较高。 在减少少年管教设施内青少年自杀方面已取得进展，最低限度 已考虑到曾被监禁的青少年的自杀风险。 青少年在重新融入社区时面临诸多挑战，这可能会增加他们的风险 据估计，60% 至 80% 的青少年有自杀行为。 严重的心理健康问题，以及在系统中度过的时间可能会加剧这些情况。 尽管如此，参与司法系统的青少年对心理健康服务的需求也是一个严重的问题。 尽管认识到这一弱势群体存在风险，但基于证据的自杀预防策略并没有 作为刑满释放青少年例行重返服务的一部分，人们对此知之甚少。 在少年司法系统中实施这些战略的成功方法。 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 纳入青少年特征和与监禁相关的背景因素（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 在这些发现和结构化实施科学框架的指导下， 该提案还将与主要利益攸关方进行干预前评估，以验证 将考虑潜在的干预选择的机器学习模型。 将模型融入实践的促进因素和障碍，以及何时、如何以及在何处进行干预 实现本拟议研究的目标将提供有针对性的干预。 关于少年司法系统中高危青少年自杀的建议 本提案。 还将支持 Ruch 博士的培训，他致力于减少青少年自杀的研究事业 Ruch 博士的培训计划包括：（1）获得机器学习和技术方面的技能。 预测建模技术可以更准确地识别自杀风险并为有针对性的预防提供信息 司法系统中的青年人 (2) 提高自杀预防干预措施的知识，包括健康知识 服务系统，以了解医疗保健实践和政策如何促进或阻碍干预 对于参与司法系统的青年，以及 (3) 加强实施科学和 先进的定性研究方法弥合了研究与实践之间的差距。 将通过引入创新的自杀技术方法来进一步推进青少年自杀研究领域 在高风险和服务不足的人群中进行预防，同时也产生关于 在资源可能有限的少年司法系统中，执行工作面临着独特的挑战。