背景:倦怠通常被定义为一种情绪状态,物理,和影响各种职业的人的精神疲惫(例如医生,护士,teachers).倦怠的后果包括动机下降,生产力,整体幸福感下降。因此,基于机器学习的倦怠预测已成为最近研究的重点。在这项研究中,目的是使用机器学习检测职业倦怠,并在匈牙利高中教师样本中确定其最重要的预测因素.
方法:最终样本包括1,576名高中教师(522名男性),他完成了一项调查,包括各种社会人口统计学和健康相关问题以及心理问卷。具体来说,抑郁症,失眠,互联网习惯(例如何时以及为什么使用互联网)和有问题的互联网使用是本研究中测试的最重要的预测因素之一。对监督分类算法进行了训练,以检测由两个众所周知的倦怠问卷评估的倦怠。使用递归特征消除进行特征选择。通过具有10倍交叉验证的网格搜索来调整超参数。由于阶级不平衡,类权重(即成本敏感学习),在单独的分析中应用了降采样和混合方法(SMOTE-ENN)。最终的模型评估是在先前未看到的保持测试样品上进行的。
结果:在最终数据集中的19.7%的教师中检测到倦怠。保持测试样本的最佳预测性能是通过具有类别weigths的随机森林实现的(AUC=0.811;平衡准确度=0.745,灵敏度=0.765;特异性=0.726)。倦怠的最佳预测因子是贝克的抑郁量表得分,Athen的失眠量表评分,有问题的互联网使用问卷的子量表和自我报告的当前健康状况。
结论:算法的性能与以前的研究相当;然而,重要的是要注意,我们在以前看不见的保留样本上测试了我们的模型,这表明了更高的泛化水平。另一个值得注意的发现是,除了抑郁和失眠,其他变量,例如有问题的互联网使用和在线花费的时间,也被证明是职业倦怠的重要预测因素。
BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers.
METHODS: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 10-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample.
RESULTS: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = 0.811; balanced accuracy = 0.745, sensitivity = 0.765; specificity = 0.726). The best predictors of burnout were Beck\'s Depression Inventory scores, Athen\'s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status.
CONCLUSIONS: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.