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增长提供了关键的途径，但由于对心理健康服务的需求未满足，特别是对于抑郁和焦虑，可以减轻这种机会特别是对于种族/族裔少数民族学生。心理健康问题与高食物率相交不安全感，住房不安全感和无家可归，以创造一个相互加剧的残疾周期和损害。可与社会协同管理心理健康需求的可扩展和有效的护理系统精神健康决定因素非常需要。签名项目旨在优化可扩展的护理系统，称为平台，低收入，在东洛杉矶学院高度多样化的样本，而同时推进个性化心理保健的科学。更大的个性化工具临床决策为减少流失和的治疗提供了更高效，更有效的分配改善症状和功能性结果。个性化包括选择适当的护理水平在最初的演讲中，知道何时需要更改护理水平。这是第一个解决的研究个性化护理的工具，适合各种社区大学生的需求。签名项目的目的是比较治疗方法进行分类和适应（仅基于症状严重程度），以从四个重叠和相互加强的结构：心理健康的社会决定因素；早期的逆境和生活压力；诱人，启用和需要影响卫生服务使用的影响；和全面的心理健康状况。随着时间的推移，基线和轨迹的静态特征将用于分配到初始护理水平（范围从自我指导的在线预防到教练指导的在线治疗，再到临床医生提供护理）并告知在40周内适应护理（例如，保持课程，提高护理水平，重新启动护理水平）。 ELAC学生将被随机分组（每年n = 200，总n = 1000）以基于症状严重程度的决策或数据驱动的决策。我们将评估数据驱动算法是否提高了依从性，抑郁和焦虑以及功能。这些措施和算法将根据统计预测，核心健康差异/文化能力组的审查，以及来自健康思维调查的观察数据来自加利福尼亚州的10所社区大学（包括Elac）。最终目标是优化ELAC学生的展位计划的有效性，同时创建个性化模板以维持并传播到其他社区大学，以及生成将为实施成本效益和投资预测预测提供信息的数据站立区或全州范围内。