在社区诊所开发ADHD干预的实施模型:利用人工智能和数字技术
Margaret H Sibley1, Leonard Bickman2, David Atkins3
1University of Washington School of Medicine, Seattle Children's Research Institute.
Cognitive and behavioral practice
|January 1, 2025
概括
这项研究引入了一个新的基于社区的模型,用于青少年ADHD行为治疗 (支持青少年的自主日常;STAND),增强了人工智能 (AI) 用于忠诚度监测. 人工智能工具对改善现实环境中的治疗提供有希望.
科学领域:
- 行为科学 行为科学
- 临床心理学 临床心理学
- 实施科学 实施科学
背景情况:
- 在社区环境中实施注意力缺陷/多动障碍 (ADHD) 行为疗法存在重大挑战.
- 现有的模型往往难以实现可扩展性和一致的交付.
研究的目的:
- 开发和评估一个以社区为基础的青少年多动症行为治疗实施模式,名为支持青少年的自主日报 (STAND).
- 整合动机面试 (MI) 并利用人工智能 (AI) 来增强支持和忠诚度监控.
主要方法:
- 使用探索,准备,实施,维持 (EPIS) 框架的利益相关者参与的开发方法.
- 整合任务转移监督,两周一次的技术援助,MI完整性监测的AI,以及STAND忠实性的AI生成指标.
- 在临床医生的仪表板上数字化资源,以视觉反显示器和与治疗师,青年和家长进行试点测试.
主要成果:
- 开发的STAND模型展示了最初的可行性,可接受性和机构参与.
- 人工智能驱动的STAND忠实度测量工具在试点评估中显示出有希望的结果.
- 利益相关者的反强调了修订后的服务交付模式的优点和缺点.
结论:
- 基于社区的STAND模型,增强了AI,为提供青少年ADHD行为治疗提供了一个有希望的方法.
- 代开发和测试对于完善模型及其AI组件以实现更广泛的实施至关重要.
- 这项研究支持AI在监测和提高社区环境中行为干预措施的可靠性方面的潜力.
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