使用机器学习来简化大学人格清单 (UPI)
Weihua Guo1, Jinsheng Hu1, Qi Qiang1
1Department of Psychology, Liaoning Normal University, Dalian 116029, China.
Acta psychologica
|December 5, 2024
概括
机器学习简化了大学人格库存 (UPI) 从60到6项,达到89.4%的准确性. 这种快速心理健康查工具有助于大学生及时进行自我评估和干预.
科学领域:
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
- 心理健康评估 心理健康评估
背景情况:
- 快速诊断心理健康问题对于大学生来说至关重要.
- 大学人格清单 (UPI) 是一个常见但漫长的评估工具.
- 目前的UPI的局限性需要一种更有效的选方法.
研究的目的:
- 开发一种机器学习模型,用于预测简化的 UPI 项目.
- 为大学生创建一个快速有效的心理健康查工具.
主要方法:
- 为5155名大学生提供了UPI,以构建数据集.
- 比较支向量机,随机森林和K-最近邻近模型.
- 分析了单个和组合的特征,以优化模型.
主要成果:
- 随机森林模型实现了最高的准确度,达到89.4%.
- 统一产品标签尺度大幅降低了90%,从60项降至6项.
- 四个关键项目 (敏感情绪,身体疲劳,自卑感,无缘无故的不安) 显示出高稳定性 (>90%的发生率).
结论:
- 机器学习有效地简化了像UPI这样的长时间测量工具.
- 简化的UPI使大学生能够更快,更方便地进行自我评估.
- 这种工具有助于及时进行心理健康干预和支持.
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