心理问题,互联网成和生活质量预测匈牙利教师的倦怠:机器学习分析
Gergely Feher1, Krisztian Kapus1, Antal Tibold1
1Centre for Occupational Medicine, Medical School, University of Pécs, Pécs, Hungary.
BMC public health
|August 27, 2024
概括
机器学习模型准确地预测了匈牙利高中教师的倦怠. 关键预测因素包括抑郁症,失眠和有问题的互联网使用,突出了针对性干预的必要性.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 教育教育教育教育教育教育.
背景情况:
- 倦怠的特点是情绪,身体和精神疲劳,显著影响像教师这样的专业人士.
- 倦怠的后果包括动机,生产力和整体幸福感的降低.
- 机器学习方法越来越多地用于预测倦怠和了解其驱动因素.
研究的目的:
- 开发和评估机器学习模型,用于检测匈牙利高中教师的倦怠.
- 确定在这个特定的专业群体中导致倦怠的最重要的预测因素.
主要方法:
- 分析了1576名高中教师的数据集,包括社会人口统计,健康和心理数据.
- 采用监督分类算法,通过递归特征消除进行特征选择,并使用网格搜索和交叉验证进行超参数调整.
- 诸如类权重,下方样本和SMOTE-ENN等技术被用来解决类不平衡.
主要成果:
- 在19.7%的参与教师中发现了倦怠症.
- 使用类权重的随机森林模型实现了最高的预测性能 (AUC=0.811).
- 重要的预测因素包括抑郁症 (贝克的抑郁症库存),失眠 (雅典失眠量表),有问题的互联网使用和自我报告的健康状况.
结论:
- 机器学习模型证明了教师的有效燃烧预测,结果与以前的研究相似,但在未见过的数据上进行了测试,以提高概括性.
- 除了抑郁症和失眠等传统因素之外,问题性的互联网使用和在线花费的时间成为关键的倦怠预测因素.
- 这些发现强调了在解决教师倦怠问题时考虑数字习惯和心理健康的重要性.
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