Centre for Occupational Medicine, Medical School, University of Pécs, Pécs, Hungary.
Department of Labour Law and Social Security Law, Faculty of Law, University of Pécs, Pécs, Hungary.
BMC Public Health. 2024 Aug 27;24(1):2322. doi: 10.1186/s12889-024-19797-9.
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.
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.
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.
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.
burnout 通常被定义为一种情绪、身体和精神疲惫的状态，影响着各种职业的人（如医生、护士、教师）。 burnout 的后果包括降低动机、生产力和整体幸福感下降。因此，基于机器学习的 burnout 预测已成为最近研究的焦点。在这项研究中，我们的目的是使用机器学习来检测 burnout，并在匈牙利高中教师样本中识别其最重要的预测因素。
最终样本由 1576 名高中教师（522 名男性）组成，他们完成了一项包括各种社会人口学和健康相关问题以及心理问卷的调查。具体来说，抑郁、失眠、上网习惯（例如何时以及为何使用互联网）和上网问题是本研究中测试的最重要的预测因素之一。我们训练了监督分类算法来检测由两个著名的 burnout 问卷评估的 burnout。使用递归特征消除进行特征选择。通过 10 倍交叉验证的网格搜索调整超参数。由于类不平衡，在单独的分析中应用了类权重（即代价敏感学习）、下采样和混合方法（SMOTE-ENN）。最终模型评估是在以前未见过的保留测试样本上进行的。
在最终数据集的教师中，有 19.7%被检测出 burnout。在保留测试样本上，具有类权重的随机森林的预测性能最佳（AUC=0.811；平衡准确性=0.745，敏感性=0.765；特异性=0.726）。 burnout 的最佳预测因素是 Beck 抑郁量表评分、Athen 失眠量表评分、问题性互联网使用问卷的子量表以及自我报告的当前健康状况。
算法的性能与以前的研究相当；然而，值得注意的是，我们在以前未见过的保留样本上测试了我们的模型，这表明了更高的泛化能力。另一个显著的发现是，除了抑郁和失眠之外，其他变量，如上网问题和上网时间，也被证明是 burnout 的重要预测因素。
