开发和验证用于精神病的认知行为疗法在线培训与自动反自动化反
Sarah L Kopelovich1, Rachel M Brian1, Mike Tanana
1Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine.
Psychotherapy (Chicago, Ill.)
|March 17, 2025
概括
开发了一个在线工具,用于训练用于精神病的认知行为疗法 (CBTp),使用机器学习来评估技能忠诚度. 这一创新使得标准化,异步培训和自动反能够改善CBTp的实施.
科学领域:
- 心理学 心理学 心理学
- 精神病学是一个精神病学.
- 人工智能的人工智能
背景情况:
- 实施基于证据的心理治疗,如CBTp需要可访问的培训和忠诚度评估.
- 当前的培训方法可能缺乏标准化和可扩展性,阻碍了广泛采用.
研究的目的:
- 开发和评估CBTp的在线培训工具,结合行为排练和机器学习进行忠实性评估.
- 与人类评分员相比,评估自动化技能评估的可靠性和准确性.
主要方法:
- 设计了一个在线CBTp培训模块,包含教学,模拟客户端和行为排练任务.
- 在3,958个口头响应上训练了一个基于变压器的机器学习模型,以评估技能忠实度.
- 收集的人类评级和计算的类内相关性 (ICC) 和ML和人类评估之间的协议百分比.
主要成果:
- 技能评级的人类ICC很高 (平均ICC=0.77).
- 机器学习模型与人类忠实度评分 (r = .74) 和高达成一致 (96%) 的高相关性.
- 模拟的客户端真实性评级超过了基准.
结论:
- 使用ML对离散CBTp技能进行自动评估是可行的和可靠的.
- 开发的工具支持标准化,异步培训,并为CBTp提供自动反.
- 这种方法可以提高CBTp的实施和可持续性.
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