Methods Core

方法核心的具体目标是支持共享基础设施，从而支持EtuDS 调查人员将有效地执行拟议的签名R01，其中包括 青少年风险“(部分)和”帮助高危青少年的综合护理“(ICHART)随机对照试验，以及三种R34疗法 在我们目前的P50中心奖的竞争续签中描述的开发研究。这一核心将 还推动了几项新兴技术的发展，以识别有自杀风险的青少年，并通过一种特殊的 专注于吸引黑人青年；促进健康公平；支持和扩大职业早期人数 从事青少年自杀预防研究的科学家(ECS)；并担任地方和国家机构 资源共享、研究专长和新知识。Core将提供可操作、分析、 通过以下方式实现这些目标的发展支助：(1)共享数据管理和统计 协作，在所有练习生中心研究中提供一致、高效的支持；(2)使用经验证的- 有效、符合HIPAA且可扩展的方法来识别和同意研究患者并满足招募要求 目标不增加忙碌的提供者和办公室工作人员的负担；(3)支持集中、监督良好、高度集中的 训练有素、有文化能力的评审员团队，他们将确认受试者资格，管理标准化的 一系列基线和后续评估，并促进受试者留存；(4)支持新兴智能手机 研究方法包括智能手机数据的移动传感和对 21世纪；(5)自由文本非结构化电子文档的机器学习和自然语言处理 预测自杀行为的健康记录；(6)定性研究方法和以人为本的专业知识 旨在让患者、护理人员和提供者参与进来，反复改进我们的干预措施，并向新的 中心项目；以及(7)实施科学方法的应用，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、照顾者和提供者的需求 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.