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开发社区诊所多动症干预的实施模型:利用人工智能和数字技术

Developing an Implementation Model for ADHD Intervention in Community Clinics: Leveraging Artificial Intelligence and Digital Technology.

作者信息

Sibley Margaret H, Bickman Leonard, Atkins David, Tanana Michael, Coxe Stefany, Ortiz Mercedes, Martin Pablo, King Julian, Monroy Jessica M, Ponce Teodora, Cheng Jenny, Pace Brian, Zhao Xin, Chawla Varun, Page Timothy F

机构信息

University of Washington School of Medicine, Seattle Children's Research Institute.

Center for Children & Families, Florida International University, Ontrak Health Inc., Henderson, NV.

出版信息

Cogn Behav Pract. 2024 Nov;31(4):482-497. doi: 10.1016/j.cbpra.2023.02.001. Epub 2023 Mar 3.

DOI:10.1016/j.cbpra.2023.02.001
PMID:39741999
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11684772/
Abstract

Implementation of behavior therapy for ADHD faces challenges in community settings. We describe development of a community-based implementation model for adolescent ADHD behavior therapy (Supporting Teens' Autonomy Daily; STAND) blended with Motivational Interviewing (MI). A stakeholder-engaged development approach is used based on the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Resulting model features include: (a) task-shifting supervision from experts to agency supervisors, (b) holding bi-weekly technical assistance drop-ins to provide training and implementation supports, (c) MI integrity monitoring and feedback by artificial intelligence (AI), (d) AI-generated metrics for STAND content fidelity, (e) digitizing resources (manual, worksheets, tips, videos) on a clinician dashboard, (f) creating visual displays of feedback using badges and graphs, and (g) adding a rapport-building session prior to manualized content. We conducted stakeholder focus groups (=32) and two pilot studies to evaluate the new STAND AI measurement tool and revised service-delivery model (=6 therapists, 7 youth and parents, 3 agency supervisors). Results revealed advantages and disadvantages of the model, supported the promise of a STAND AI fidelity measurement tool, and indicated initial feasibility, acceptability, and agency engagement in STAND's community-based implementation model. We discuss future directions for continued iterative development and testing. Video examples are included as supplementary material.

摘要

在社区环境中实施注意力缺陷多动障碍(ADHD)行为疗法面临挑战。我们描述了一种基于社区的青少年ADHD行为疗法实施模型(每日支持青少年自主;STAND)的开发,该模型融合了动机性访谈(MI)。基于探索、准备、实施、维持(EPIS)框架采用了利益相关者参与的开发方法。最终模型的特点包括:(a)将监督任务从专家转移到机构主管,(b)每两周进行一次技术援助即席指导,以提供培训和实施支持,(c)通过人工智能(AI)监测MI的完整性并提供反馈,(d)AI生成的STAND内容保真度指标,(e)在临床医生仪表板上对资源(手册、工作表、提示、视频)进行数字化处理,(f)使用徽章和图表创建反馈的可视化展示,以及(g)在手册化内容之前增加一个建立融洽关系的环节。我们进行了利益相关者焦点小组(n = 32)和两项试点研究,以评估新的STAND AI测量工具和修订后的服务提供模式(6名治疗师、7名青少年及其父母、3名机构主管)。结果揭示了该模型的优缺点,支持了STAND AI保真度测量工具的前景,并表明了STAND基于社区的实施模型的初步可行性、可接受性以及机构参与度。我们讨论了持续迭代开发和测试的未来方向。视频示例作为补充材料包含在内。

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Front Health Serv. 2022 Aug 17;2:929521. doi: 10.3389/frhs.2022.929521. eCollection 2022.
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Multi-label Multi-task Deep Learning for Behavioral Coding.用于行为编码的多标签多任务深度学习
IEEE Trans Affect Comput. 2022 Jan-Mar;13(1):508-518. doi: 10.1109/taffc.2019.2952113. Epub 2019 Nov 8.
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Engagement Barriers to Behavior Therapy for Adolescent ADHD.青少年注意力缺陷多动障碍行为治疗的参与障碍。
J Clin Child Adolesc Psychol. 2023 Nov-Dec;52(6):834-849. doi: 10.1080/15374416.2022.2025597. Epub 2022 Jan 27.
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Building a Theoretical Model for Supporting Teens' Autonomy Daily (STAND): A Network Analysis of Family-Perceived Changes.构建支持青少年自主性日常的理论模型(STAND):家庭感知变化的网络分析。
Behav Ther. 2022 Jan;53(1):49-63. doi: 10.1016/j.beth.2021.05.009. Epub 2021 Jun 10.
5
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Predictors of Treatment Engagement and Outcome Among Adolescents With Attention-Deficit/Hyperactivity Disorder: An Integrative Data Analysis.预测青少年注意力缺陷/多动障碍治疗参与度和结局的因素:综合数据分析。
J Am Acad Child Adolesc Psychiatry. 2022 Jan;61(1):66-79. doi: 10.1016/j.jaac.2021.03.017. Epub 2021 Jun 5.
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