Screening, Tracking and Treatment for Anxiety and Depression in Community Colleges

社区大学焦虑症和抑郁症的筛查、跟踪和治疗

• 批准号: 10406817
• 负责人: MICHELLE G CRASKE
• 金额: $ 238.54万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 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/10406817
• 关键词: Address; Adherence; Administrator; Advocacy; Affect; Algorithms; Anxiety; Area; Awareness; Behavioral; Budgets; California; Caring; Chronic; Clinical; Clinical Sciences; Cognitive; Cognitive Therapy; Collaborations; Communities; County; Data; Data Science; Decision Making; Detection; Economics; Education; Effectiveness; Ethnic Origin; Evaluation; Financial Hardship; Funding; Geography; Goals; Health Care Costs; Health Resources; Health Services; Health Status; Healthcare Systems; Hearing; Housing; Investigation; Investments; Learning; Life; Life Stress; Los Angeles; Low income; Mental Depression; Mental Health; Mental Health Services; Methods; Mind; Modeling; Outcome; Pathway interactions; Performance; Persons; Pilot Projects; Policy Maker; Population; Prevention; Provider; Psyche structure; Public Health; Quality of Care; Race; Research Personnel; Research Project Grants; Research Support; Resources; Risk; Risk Reduction; Sampling; Science; Self Efficacy; Self-Injurious Behavior; Services; Severities; Source; Students; Suicide; Suicide prevention; Supervision; Surveys; Symptoms; System; Testing; Training; Triage; Unemployment; Update; anxiety treatment; behavior influence; care systems; cohort; college; community barrier; community college; cost effectiveness; cultural competence; data streams; digital mental health; early life adversity; ethnic minority; evidence base; falls; food insecurity; health disparity; health literacy; higher education; implementation barriers; implementation efforts; implementation facilitators; implementation framework; implementation science; improved; innovation; minority student; operation; peer coaching; pilot trial; prediction algorithm; predictive modeling; programs; racial and ethnic; rapid detection; recruit; reduce symptoms; satisfaction; screening; social determinants; social stigma; socioeconomics; substance use; success; sustainability framework; tool; treatment response; university student; uptake; user centered design

Overall Component Abstract Community colleges (CCs) are primary engines for economic advancement for persons from lower socio- economic backgrounds, but the promise of higher education is often thwarted by untreated mental health problems, especially among racial/ethnic minority students. Alarmingly high rates of depression and anxiety on community college campuses collide with daunting life challenges (such as early adversity, housing and food insecurity) and inadequate mental health resources. Untreated depression and anxiety can have dire consequences, extending from poor academic performance to suicide. To address the enormous mental health gap in a low income, highly diverse sample of CC students at East Los Angeles College (ELAC), we propose to evaluate a scalable, efficient, and evidence-based system of care called STAND for screening, tracking and treating anxiety and depression. STAND uses a stratified stepped care model, ranging from self-guided online cognitive behavioral prevention, to online cognitive behavioral therapy with coaching, to clinician-delivered care. Continuous tracking enables treatment adaptation as needs evolve plus rapid detection and management of suicidality. The STAND system was implemented on the UCLA campus from late 2017- early 2020. Since Fall 2019, we have been collaborating with ELAC administrators and students to adapt STAND for their needs, and have an ongoing pilot project funded through the Los Angeles County Department of Mental Health (2020-2022). This pilot lays the groundwork for ALACRITY in which we will optimize STAND at ELAC (n=1000) with continuing support from DMH and we will explore sustainability and spread to other CCs. The ALACRITY center focuses upon (1) optimizing effectiveness through multivariate predictive models, including social determinants of mental health, for improving stepped care triaging, adaptation, and risk detection, which will simultaneously advance the science of personalized mental health, (2) optimizing implementation through exploratory projects, pilot trials and Methods Scientific Area hubs that primarily target uptake and engagement as well as integration and cultural competency to meet the needs of this underserved, diverse student population, and (3) exploring sustainability via centralized state-wide data-streams, cost-effectiveness and return-on-investment projections for STAND implementation, investigation of generalizability, and exploration of barriers and facilitators of implementation across geographically diverse CCCs, that inform discussions with policy makers and stakeholders. The overall approach is guided by the Accelerated Creation to Sustainment implementation framework which allows for continuous evaluation and redesign over each annual cohort. The Administrative Core will oversee operations of the Center, pilot study program, training of junior investigators, and sustainability planning. The Methods Core will support research projects, implementation efforts, and innovative and synergistic collaborations that focus on increasing engagement of stakeholders. Our long-term goal is to optimize STAND through personalization tools and facilitate expansion of STAND’s reach across CCs statewide and ultimately nationwide.

总体组件摘要 社区学院（CC）是低社会阶层人士经济发展的主要引擎， 经济背景，但高等教育的承诺往往受到未经治疗的心理健康的阻碍 问题，特别是种族/少数民族学生。抑郁症和焦虑症的发病率高得惊人， 社区大学校园与令人生畏的生活挑战（如早期的逆境，住房和食物）发生冲突 不安全）和精神卫生资源不足。未经治疗的抑郁症和焦虑症会导致可怕的 结果，从学习成绩差到自杀。为了解决巨大的心理健康问题， 在东洛杉矶学院（ELAC）的低收入，高度多样化的CC学生样本的差距，我们建议 评估一个可扩展的，有效的，以证据为基础的护理系统，称为STAND，用于筛查，跟踪和 治疗焦虑和抑郁STAND采用分层阶梯式护理模式， 认知行为预防，到在线认知行为治疗与指导，到临床医生提供的护理。 持续跟踪使治疗适应需求的发展，加上快速检测和管理 自杀倾向STAND系统于2017年底至2020年初在UCLA校园实施。毁灭之后 2019年，我们一直在与ELAC管理员和学生合作，以适应他们的需求， 通过洛杉矶县精神卫生部资助正在进行的试点项目（2020-2022）。 该试验为ALACRITY奠定了基础，我们将在ELAC（n=1000）上优化STAND， DMH的支持，我们将探索可持续性并推广到其他CC。ALACRITY中心专注于 （1）通过多变量预测模型优化有效性，包括心理健康的社会决定因素， 健康，以改善分级护理分诊，适应和风险检测，这将同时推进 个性化心理健康的科学，（2）通过探索性项目，试点试验优化实施 和方法科学区中心，主要针对吸收和参与以及整合和文化 能力，以满足这一服务不足，多样化的学生群体的需求，（3）探索可持续发展 通过集中的全州数据流，STAND的成本效益和投资回报预测 实施、调查可推广性以及探讨实施的障碍和促进因素 在不同地理区域的核心共同承诺中，为决策者和利益攸关方的讨论提供信息。整体 该方法以加速创建到可持续实施框架为指导，该框架允许 对每个年度组群进行持续评估和重新设计。行政核心将监督业务 该中心，试点研究计划，初级研究人员的培训和可持续发展规划。核心方法 将支持研究项目、实施工作以及创新和协同合作， 加强利益攸关方的参与。我们的长期目标是通过个性化来优化STAND 工具，并促进STAND在全州乃至全国的CC范围内的扩展。