Signature Project

招牌项目

基本信息

批准号：
10615204
负责人：
MICHELLE G CRASKE
金额：
$ 41.54万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10615204
关键词：
Acculturation Acute Address Adherence Algorithms Anxiety Back California Caring Characteristics Cognitive Therapy Critical Pathways Data Decision Making Discrimination Effectiveness Employment Enrollment Ethnic Origin Family Gender Generations Geography Goals Health Status Home Homelessness Housing Immigration Impairment Income Individual Investments Lead Life Life Stress Los Angeles Low income Managed Care Measures Mental Depression Mental Health Mental Health Services Methods Mind Monitor Neurocognitive Observational Study Occupational Outcome Population Prevention Race Randomized Recording of previous events Sampling Science Severities Sex Orientation Social support Statistical Models Suicide Surveys Symptoms Testing Time Time Study Treatment outcome Triage Workforce Development active method adaptation algorithm algorithm development anxiety symptoms anxiety treatment automated algorithm care systems clinical decision-making college community college comorbidity cost effectiveness cultural competence decision making algorithm depressive symptoms design disability early life adversity emotion dysregulation ethnic minority food insecurity food security functional outcomes health determinants health disparity health service use implementation framework improved minority student personalized care prediction algorithm preference programs racial minority response screening social social determinants sociodemographics socioeconomics standard of care stressor symptomatic improvement theories tool treatment adherence underserved community university student

项目摘要

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.

签名项目摘要社区大学为劳动力发展和社会经济收益提供了重要途径，但是由于对心理健康服务的需求未得到满足，特别是针对抑郁和焦虑的需求，这一机会被削弱了，特别是对于少数族裔学生。心理健康问题与高食品摄入率交织在一起不安全、住房不安全和无家可归，造成残疾和无家可归的相互加剧的循环损害。一个可扩展且有效的护理系统，可根据社会需求管理心理健康需求迫切需要心理健康决定因素。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，同时同时推进个性化心理保健科学。工具更加个性化临床决策提供了更高效和更有效的治疗分配，以减少损耗和改善症状和功能结果。个性化包括选择适当的护理水平在初次就诊时并了解何时需要改变护理水平。这是第一项研究来解决适合不同社区学院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类和治疗的护理方法标准适应（仅基于症状严重程度）数据驱动的算法，该算法从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；对卫生服务使用的诱发、促成和需求影响；和综合心理健康状况。基线的静态特征以及随时间变化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，到临床医生提供的护理）并告知 40 周内调整护理（例如坚持到底、提高护理水平、重新开始护理）。 ELAC学生将被随机分配（每年 N = 200，总 N = 1000）以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可以提高依从性、症状抑郁和焦虑以及功能。这些措施和算法将每年根据情况进行完善统计预测，由方法核心健康差异/文化能力小组审查，以及来自加州 10 所社区大学健康心态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，同时创建个性化模板以维持并传播到其他社区大学，以及生成数据，为实施的成本效益和投资回报预测提供信息 STAND 区域或全州范围。