Signature Project

签名项目摘要 社区学院为劳动力发展和社会经济收益提供了重要途径，但 这一机会因未满足的心理健康服务需求而减少，特别是抑郁和焦虑， 尤其是少数民族学生。心理健康问题与高食物摄入率交织在一起 无保障、住房无保障和无家可归，造成残疾和 损伤一个可扩展的和有效的护理系统，与社会合作管理心理健康需求， 心理健康的决定因素是迫切需要的。签名项目旨在优化以下方面的有效性： 可扩展的护理系统，称为STAND，在低收入，高度多样化的样本在东洛杉矶学院，而 同时推进个性化心理健康护理科学。更个性化的工具， 临床决策提供了更有效和更有效的治疗分配，减少损耗， 改善症状和功能结果。个性化包括选择适当的护理水平 在最初的介绍和知道什么时候需要改变护理水平。这是第一项研究， 适合不同社区大学生需求的个性化护理工具。 签名项目的目标是比较治疗分诊的护理标准方法， 适应（仅基于症状严重程度）数据驱动的算法，从四个重叠和 相互加强的结构：心理健康的社会决定因素;早期逆境和生活压力; 对卫生服务使用的易感性、使能性和需求影响;以及综合心理健康状况。 基线时的静态特征以及随时间推移的轨迹将用于分配初始护理水平（范围 从自我指导的在线预防，到教练指导的在线治疗，再到临床医生提供的护理）， 护理的适应（例如，坚持到底，提高护理水平，重新开始护理）超过40周。ELAC学生 将被随机分配（每年N=200，总N=1000）至基于症状严重程度的决策或数据- 驱动决策。我们将评估数据驱动的算法是否能改善依从性、 抑郁和焦虑，以及功能。这些措施和算法将每年根据以下方面进行改进： 统计预测，由方法核心健康差异/文化能力小组审查，以及 来自加州10所社区学院的健康心理调查结果的观察数据 （包括ELAC）。最终目标是优化ELAC学生STAND计划的有效性， 同时为个性化创建模板，以维持并传播到其他社区学院， 生成数据，为执行 全国或全国范围内。

Signature Project Abstract Community colleges provide a critical pathway for workforce development and socio-economic gain, but this opportunity is mitigated by unmet need for mental health services, particularly for depression and anxiety, and particularly for racial/ethnic minority students. Mental health problems intersect with high rates of food insecurity, housing insecurity and homelessness to create a mutually exacerbating cycle of disability and impairment. A scalable and effective system of care that manages mental health needs in concert with social mental health determinants is sorely needed. The Signature Project aims to optimize the effectiveness of scalable system of care, called STAND, in low income, highly diverse sample at East Los Angeles College, while concurrently advancing the science of personalized mental health care. Greater personalization of tools for clinical decision-making offers more efficient and more effective allocation to treatments that reduce attrition and improve symptomatic and functional outcomes. Personalization includes selecting the appropriate level of care at initial presentation and knowing when a change to level of care is needed. This is the first study to address tools for personalizing care that are suited to the needs of diverse community college students. The goal of the Signature Project is to compare standard of care approaches for treatment triaging and adaptation (based on symptom severity alone) to data-driven algorithms that draw from four overlapping and mutually reinforcing constructs: social determinants of mental health; early adversity and life stress; predisposing, enabling and need influences upon health services use; and comprehensive mental health status. Static features at baseline as well as trajectories over time will be used to allocate to initial level of care (ranging from self-guided online prevention, to coach-guided online therapy, to clinician delivered care) and inform adaptation of care (e.g., stay the course, increase level of care, re-initiate care) over 40 weeks. ELAC students will be randomized (N=200 per year, total N=1000) to either symptom severity-based decision making or to data- driven decision making. We will evaluate whether the data-driven algorithms improve adherence, symptoms of depression and anxiety, and functioning. The measures and algorithms will be refined annually based on statistical prediction, review by the Methods Core Health Disparities/Cultural Competency group, and observational data from the Healthy Minds Survey results from 10 community colleges across California (including ELAC). The end goals are to optimize the effectiveness of the STAND program for ELAC students, while creating templates for personalization to be sustained and spread to other community colleges, and generating data that will inform cost-effectiveness and return-on-investment projections for implementation of STAND region or state-wide.