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Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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一种基于机器学习的方法,用于开发中国症状检查清单-11 (CSCL-11)

Xuanyi Cai1, Yunan Zhang2, Meng Su3

  • 1School of Management, Beijing University of Chinese Medicine, Beijing 102488, China.

Behavioral sciences (Basel, Switzerland)
|April 26, 2025
PubMed
概括

一个新的11项尺度,中国症状检查清单-11 (CSCL-11),有效地选了中国的心理障碍. 它准确地识别高风险个体,优于更长版本和其他短尺度.

关键词:
这是SCL-90的中文版本.机器学习是机器学习.选仪器 选仪器 选仪器变量聚类变量聚类.

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科学领域:

  • 精神病学和心理学 精神病学和心理学
  • 医疗保健中的机器学习
  • 心理测量仪器开发

背景情况:

  • 中国症状检查清单-90 (SCL-90) 很长,导致遵守率很低.
  • 需要一个更简短,更有效的查工具,用于中国的普通人口.

研究的目的:

  • 开发一个简洁的精神病理查仪器,使用中国SCL-90项目的子集.
  • 使用机器学习识别患有心理障碍高风险的个体.

主要方法:

  • 应用了变量聚类来从SCL-90中选择11个代表性项目.
  • 使用因子分析验证了新尺度的心理特征.
  • 机器学习模型被用于评估预测性表现和识别高风险个体.

主要成果:

  • 由此产生的11项尺度,中国症状检查表-11 (CSCL-11),显示出高的内部一致性 (克朗巴赫的α=0.84).
  • CSCL-11对全球严重性指数 (GSI) 评分表现出强大的预测准确性 (R2 = 0.92),并以96%的准确性确定了高风险个体.
  • 在预测GSI分数和识别高风险群体方面,CSCL-11的表现优于其他较短的尺度 (SCL-14,SCL-K11,SCL-K-9).

结论:

  • CSCL-11是一个简洁而有效的初步查工具,用于中国普通人口的精神病理学.
  • 它保留了SCL-90的基本信息,同时提高了完成率和效率.
  • CSCL-11为大规模的心理查提供了一个有价值的替代方案.