Leveraging Implementation Science and Design Methods to Sustain Community-Based Mental Health Services for Refugees

全世界难民人数空前增加，约有26 2019年底，数百万男女和儿童被迫离开家园。一半以上的 全世界的难民居住在人口密集的城市地区或中低收入国家的难民营。更小的 在高收入国家永久重新定居的人所占百分比。在这些背景下，难民人数增加， 常见精神疾病的发病率。战争和被迫移徙也造成社会秩序的系统性破坏， 家庭分离、紧张和冲突以及社交网络的丧失等关系。有一个 关于难民心理健康和心理社会需求的模型的证据基础正在形成，但存在重大差距 关于如何使基于证据的干预措施适应难民人口并在 社区设置。为了解决这些差距，我的职业目标是建立一个独立的研究计划 重点是在全国各地为难民社区提供基于社区的心理健康服务， 位移连续体通过与社区合作伙伴的合作，我的目标是研究整合 在非传统服务环境中开展基本医疗保险，使难民能够获得预防和护理干预措施， 促进积极的心理健康成果。我的研究试图解决的一个核心问题是如何最好地 调动社会和家庭资源，加强应对和福祉。这些研究重点是由以下因素形成的： 我在美国与受战争影响的人群一起工作超过十五年的临床实践经验， 在全球范围内，在芝加哥大学的博士培训和早期的研究经验。此K 01应用程序 结构，以建立我的优势，并发展新的知识和技能，在关键领域需要实现 我的长期目标。我的培训目标集中在1）实施科学方法，以适应，扩大和 在社区环境中维持EBI; 2）以人为本的设计应用于社区为基础的难民 精神卫生服务; 3）基于社区的试验设计和分析。在一位有成就的 多学科的导师团队，我将把这些技能应用到一个研究项目，重点是实施一个 同伴领导的多家庭组（MFG）预防干预，称为CAFES在两个社区为基础的 在芝加哥的一些组织中，有一些组织致力于促进难民家庭的吸收和满足其多层次的需求。使用导频 随机1型混合实施效果设计，研究目的包括：1）适应 为叙利亚难民家庭提供的多家庭CAFES模式， 组织使用ADAPT-ITT框架和以人为本的设计方法; 2）试点适应 评估实施科学领域的CAFES模型;以及3）探索心理健康的变化 成果、家庭和社区支持以及变革机制。利用新知识和 从K 01中获得的技能和试点研究数据，我将准备一份R 01提案，以进一步测试该模型 执行和有效性。

There has been an unprecedented increase in the number of refugees worldwide, with approximately 26 million men, women and children forcibly displaced from their homes at the end of 2019. More than half of the world’s refugees live in dense urban areas or refugee camps in low- and middle-income countries. A smaller percentage are permanently resettled in high-income countries. Across these contexts, refugees have elevated rates of common mental disorders. War and forced migration also contribute to systematic disruptions in social relationships including family separation, tension and conflict, and losses of social networks. There is an emerging evidence base of models regarding refugees’ mental health and psychosocial needs, but major gaps exist regarding how to adapt evidence-based interventions (EBIs) to refugee populations and sustain them in community settings. To address these gaps, my career goal is to establish an independent research program focused on the implementation of community-based mental health services for refugee communities across the displacement continuum. In collaboration with community partners, I aim to examine methods for integrating EBIs into non-traditional service settings to enable refugees’ access to prevention and care interventions that promote positive mental health outcomes. A central concern my research seeks to address is how to best mobilize social and family resources to enhance coping and wellbeing. These research priorities are shaped by my more than fifteen years of clinical practice experience working with war-affected populations in the U.S and globally, doctoral training at the University of Chicago and early research experiences. This K01 application is structured to build upon my strengths and develop new knowledge and skills in key areas needed to achieve my long-term goals. My training aims focus on 1) implementation science methods to adapt, scale up and sustain EBIs in community settings; 2) the application of human-centered design to community-based refugee mental health services; and 3) community-based trial design and analysis. With support from an accomplished multidisciplinary team of mentors, I will apply these skills to a research project that focuses on implementing a peer-led multiple family group (MFG) prevention intervention called CAFES in two community-based organizations in Chicago to promote uptake and meet the multi-level needs of refugee families. Using a pilot randomized type 1 hybrid implementation-effectiveness design, the research aims include: 1) Adapting the multiple family CAFES model for Syrian refugee families and delivery by peer providers in community-based organizations using the ADAPT-ITT framework and human-centered design methods; 2) Piloting the adapted CAFES model to assess implementation science domains; and 3) Exploring changes in mental health outcomes and family and community support and mechanisms of change. Drawing on the new knowledge and skills and pilot study data obtained from this K01, I will prepare an R01 proposal to further test the model implementation and effectiveness.