Methods Core

方法核心的具体目标是支持一个共享的基础设施， 调查人员有效地进行拟议的签名R 01，其中包括“预测和评估 青少年风险”（PART）和“帮助高危青少年的综合护理”（iCHART）RCT，以及三种R34治疗 在我们目前的P50中心奖的竞争性更新中描述的开发研究。这一核心将 还推动了几种新兴技术的发展，以确定有自杀风险的青年， 重点关注黑人青年的参与;促进健康公平;支持和扩大早期职业生涯的数量 从事青少年自杀预防研究的科学家（ECS）;并作为地方和国家 资源共享研究专长和新知识。方法核心将提供操作，分析， 和开发支持，通过以下方式实现这些目标：（1）共享数据管理和统计 合作为所有ETUDES中心研究提供一致和有效的支持;（2）使用经过验证的 有效的、符合HIPAA的、可扩展的方法，以识别和同意研究患者并满足招募要求 目标，而不会增加忙碌的供应商和办公室工作人员的负担;（3）支持一个集中的，监督良好的，高度 由经过培训且具有文化能力的评估员组成的团队，他们将确认受试者的合格性， 一系列基线和随访评估，并促进受试者保留;（4）支持新兴的智能手机 研究方法，包括智能手机数据的移动的传感和心理尸检的更新， 自由文本非结构化电子文档的机器学习和自然语言处理 健康记录来预测自杀行为;（6）定性研究方法和以人为本的专业知识 设计让患者、护理人员和提供者参与进来，并不断改进我们的干预措施， 中心项目;以及（7）应用实施科学方法，以确保我们的预测分析 方法和干预措施促进健康公平，满足患者、护理人员和提供者的需求， 儿科初级保健，以优化未来的吸收。

The Specific Aims of the Methods Core are to support a shared infrastructure that will enable ETUDES investigators to efficiently conduct the proposed Signature R01 which includes “Prediction and Assessment of Risk in Teens” (PART) and “integrated Care to Help At-Risk Teens” (iCHART) RCT, and three R34 treatment development studies described within this competitive renewal of our current P50 Center award. This Core will also advance the development of several emerging technologies to identify youth at risk for suicide with a special focus on engaging Black youth; advancing health equity; support and expand the number of early career scientists (ECSs) engaged in adolescent suicide prevention research; and serve as both a local and national resource sharing research expertise and new knowledge. The Methods Core will provide operational, analytic, and development support to achieve these aims through: (1) Shared data management and statistical collaboration to provide consistent and efficient support across all ETUDES Center studies; (2) Use of proven- effective, HIPAA-compliant and scalable methods to identify and consent study patients and meet recruitment targets without burdening busy providers and office staff; (3) Support for a centralized, well-supervised, highly trained, and culturally-competent team of assessors who will confirm subject eligibility, administer a standardized battery of baseline and follow-up assessments, and promote subject retention; (4) Support emerging smartphone research methods including mobile sensing of smartphone data and an update of the psychologic autopsy for the 21’st Century; (5) Machine learning and natural language processing of free-text unstructured electronic health records to predict suicidal behavior; (6) Expertise in qualitative research methods and human-centered design to engage patients, caregivers, and providers and iteratively improve our interventions and inform new Center projects; and (7) Application of implementation science methods to ensure our predictive analytic methods and interventions promote health equity and meet the needs of patients, caregivers, and providers in pediatric primary care to optimize future uptake.