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一条关键途径，但 这一机会因未得到满足的精神健康服务需求，特别是抑郁症和焦虑症的需求而减少， 特别是针对种族/少数民族学生。心理健康问题与高食用率交织在一起 不安全、住房不安全和无家可归，造成相互加剧的残疾和 减损。可扩展和有效的护理系统，与社会健康需求协调管理心理健康需求 心理健康的决定因素是迫切需要的。签名项目旨在优化 在东洛杉矶学院的低收入、高度多样化的样本中，可扩展的护理系统称为STAND 同时推进个性化心理健康护理的科学。更具个性化的工具 临床决策提供更高效和更有效的分配给治疗，以减少磨损和 改善症状和功能结果。个性化包括选择适当的护理级别 在最初介绍时，并知道何时需要更改护理级别。这是第一个解决的研究 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流和治疗分类的护理标准方法 适应数据驱动的算法(仅基于症状严重性)，这些算法来自四个重叠和 相辅相成的结构：心理健康的社会决定因素；早期的逆境和生活压力； 对卫生服务使用的易感性、促进性和需要影响；以及综合心理健康状况。 将使用基线上的静态要素以及随时间变化的轨迹来分配初始护理级别(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)和信息 适应护理(例如，坚持疗程、提高护理水平、重新启动护理)超过40周。ELAC学生 将随机(N=每年200，总N=1000)到基于症状严重性的决策或数据- 推动决策制定。我们将评估数据驱动的算法是否提高了遵从性、症状 抑郁和焦虑，以及功能正常。这些措施和算法将每年根据以下情况进行改进 统计预测，由核心健康差距/文化能力小组方法审查，以及 来自加利福尼亚州10所社区大学的健康心灵调查结果的观测数据 (包括ELAC)。最终目标是为ELAC学生优化STAND计划的有效性， 同时创建个性化模板以持续并推广到其他社区学院，以及 生成数据，为实施以下项目提供成本效益和投资回报预测 站在地区或全州范围内。

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.