通过机器学习和自然语言处理自动化评估多元文化取向
Simon B Goldberg1, Michael Tanana2, Shaakira Haywood Stewart3
1Department of Counseling Psychology, University of Wisconsin-Madison.
Psychotherapy (Chicago, Ill.)
|February 1, 2024
概括
机器学习可以自动对多元文化导向-绩效任务 (MCO-PT) 进行评分,这对治疗师培训具有前景. 虽然对于整体的MCO和文化机会有效,但对于文化谦卑和舒适性而言,准确性较低.
科学领域:
- 心理治疗的研究研究.
- 临床心理学 临床心理学
- 人工智能在心理健康中的作用
背景情况:
- 多元文化导向 (MCO) 对于有效的心理治疗至关重要.
- 该MCO-性能任务 (MCO-PT) 测量MCO能力,但需要手动编码.
- 自动化MCO-PT评分可以提高治疗师的培训.
研究的目的:
- 通过机器学习 (ML) 和自然语言处理 (NLP) 来评估自动化MCO-PT评分的可行性.
- 与人类编码器相比,评估基于ML的评分的可靠性.
主要方法:
- 利用ML和NLP技术分析了来自100名参与者的613个MCO-PT反应的转录.
- 将MCO领域的自动化得分与人为编码的得分进行比较:文化谦卑,机会和舒适.
主要成果:
- 机器学习模型在整体MCO (ρ = .75) 和文化机会 (ρ = .81) 中实现了接近人类的可靠性.
- 对文化谦卑 (ρ = .46) 和文化舒适 (ρ = .41) 的表现不那么强大.
结论:
- 使用ML进行MCO-PT的自动评分是可行的,并且在某些领域显示出高可靠性.
- 这种技术可能使得开发基于ML的培训工具,以帮助治疗师练习MCO技能.
- 需要进一步改进,以提高MCO的文化谦卑和舒适方面的准确性.
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