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

验证措施并揭示不同自闭症儿童的服务使用差异

基本信息

批准号：
10586306
负责人：
Sarabeth Broder-Fingert
金额：
$ 85.92万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
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：
2022-09-09 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10586306
关键词：
Address Advocate Appointment Caring Child Chinese Collaborations Communities Consensus Data Diagnosis Discrimination English Language Ensure Ethnic Origin Face Family Funding Future Geography Goals Guidelines Haitian Health Health Services Accessibility Healthcare Systems Hospitals Intervention Interview Knowledge Language Limited English Proficiency Linguistics Measures Mediator of activation protein Medication Errors Methods Multilingualism National Institute of Mental Health Outcome Parents Patients Policies Policy Maker Population Population Heterogeneity Provider Qualitative Research Race Recommendation Reporting Research Resistance School-Age Population Series Services Site Social support Strategic Planning Stress Surveys System Testing Translating adolescent with autism spectrum disorder autism spectrum disorder autistic autistic children barrier to care base care coordination design disparity reduction experience health care disparity implementation science improved innovation meetings randomized trial research to practice service utilization social health determinants social stigma theories therapy development tool treatment disparity treatment trial

项目摘要

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.

项目概要英语水平有限 (LEP) 儿童的家庭已被系统性地排除在自闭症之外谱系障碍（ASD）研究，但他们可能会遇到更大的护理障碍，因此几乎没有什么。了解哪些因素与这些差异相关，以及它们在不同人群中有何不同由于缺乏数据，制定数据驱动的策略来解决患有儿童的差异。 ASD 和 LEP 可能是一个挑战，因此，我们建议通过进行顺序研究来解决这一关键差距。混合方法研究，以了解患者、提供者和结构因素与哪些因素相关首先，与 LEP 调查研究专家团队合作，我们提议对服务不足的儿童家庭进行有史以来最大规模的多样化、多语言全国调查（n=2730）与 ASD 一起以五种语言（西班牙语、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该调查将嵌入自闭症关怀网络 (ACN) 中，该网络是一个由 20 个大型、地域不同的医院系统组成的全国网络，致力于改善自闭症谱系障碍 (ASD) 的护理。然后将使用定性访谈来更好地了解调查结果，最后基于最佳实践。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和背景中为数据和测量的使用以及政策决策提出建议，我们的结果将影响该领域。通过验证未来自闭症谱系障碍干预研究中使用的措施——从治疗试验到政策建议 – 以及生成数据作为未来缩小差距的干预目标我们的目标与 NIMH 减少 ASD 治疗差异的战略计划的努力直接一致。