Validating Measures and Unpacking Differences in Service Use for Diverse Children with Autism

项目摘要 英语水平有限的儿童（LEP）的家庭被系统地排除在自闭症之外 谱系障碍（ASD）的研究，但他们可能会遇到更大的障碍照顾。因此， 了解与这些差异相关的因素，以及它们在人群中的差异， 设置.由于缺乏数据，制定数据驱动的战略，以解决儿童的差异， ASD和LEP可能是一个挑战。因此，我们建议通过进行连续的 混合方法研究，以了解患者、提供者和结构因素与 在照顾患有LEP的家庭方面存在差异。首先，与LEP调查研究专家团队合作， 我们建议对未得到充分服务的儿童的家庭进行有史以来最大规模的多样化、多语言的全国调查（n=2730）。 与ASD一起验证测量（例如，父母压力、耻辱、歧视），以五种语言（西班牙语， 海地-克里奥尔语、越南语、普通话、英语），并使用这些措施来了解护理方面的差异 在不同的人群之间。该调查将嵌入自闭症关怀网络（ACN）， 由20个大型、地理位置不同的医院系统组成的全国网络，专注于改善ASD的护理。我们 然后将使用定性访谈，以更好地了解调查结果。最后，根据 在实施科学领域，我们将使用严格的共识方法将研究结果置于背景中， 为数据和措施的使用以及政策决策提出建议。我们的研究结果将影响该领域 通过验证将用于未来ASD干预研究的措施-从治疗试验到政策 倡议-以及编制数据，作为今后减少差距的干预目标 努力我们的目标与NIMH的战略计划直接一致，以减少ASD治疗的差异。

PROJECT SUMMARY Families of children with limited English proficiency (LEP) have been systematically excluded from autism spectrum disorder (ASD) research, yet they likely experience greater barriers to care. As a result, little is known about what factors are associated with these disparities, and how they differ across populations and settings. Because of this lack of data, developing data-driven strategies to address disparities for children with ASD and LEP can be a challenge. Thus, we propose to address this critical gap by conducting a sequential mixed methods study to understand what patient, provider, and structural factors are associated with disparities in care for families with LEP. First, in collaboration with a team of experts in LEP survey research, we propose the largest ever diverse, multi-lingual national survey (n=2730) of families of underserved children with ASD to both validate measures (e.g., parent stress, stigma, discrimination) in five languages (Spanish, Haitian-Creole, Vietnamese, Mandarin, English) and use those measures to understand disparities in care between and across populations. The survey will be embedded within the Autism Cares Network (ACN), a national network of 20 large, geographically diverse hospital systems focused on improving care for ASD. We will then use qualitative interviews to better understand survey findings. Finally, based on best practices from the field of implementation science, we will use rigorous consensus methods to place findings in context and make recommendations for data and measure use, as well as policy decisions. Our results will impact the field by validating measures to be used in future studies of ASD interventions – from treatment trials to policy initiatives – as well as by producing data to be used as future intervention targets in disparities reduction efforts. Our aims align directly with the NIMH’s Strategic Plan to reduce disparities in treatment of ASD.