Pereira M Graça, Santos Martim, Magalhães Renata, Rodrigues Cláudia, Araújo Odete, Durães Dalila
Psychology Research Centre (CIPsi), School of Psychology, University of Minho, 4710-057 Braga, Portugal.
Algoritmi Research Centre/LASI, University of Minho, 4800-058 Guimarães, Portugal.
Behav Sci (Basel). 2025 Apr 9;15(4):505. doi: 10.3390/bs15040505.
University students are at increased risk of developing burnout and psychological distress from high academic workloads and performance expectations. The purpose of this study is to analyze the relationship between psychological and lifestyle variables and academic burnout, as well as to identify burnout risk profiles in psychology students. This study used a cross-sectional design and included 274 Portuguese psychology students, the majority being undergraduates (72.6%). Participants were assessed on psychological well-being, psychological distress, difficulties in emotional regulation, type of diet, physical activity, sleep quality, and burnout. The results showed that psychological distress, difficulties in emotional regulation, and sleep quality were positively associated with burnout, while psychological well-being was negatively associated. Using machine learning algorithms, two distinct profiles were found: "Burnout Risk" and "No Risk". A total of 62 participants were identified as belonging to the burnout risk profile, showing higher levels of distress, emotional regulation difficulties, poor psychological well-being and sleep quality, pro-inflammatory diet, and less physical activity. The accuracy of the three machine learning models-Random Forest, XGBoost, and Support Vector Machine-was 95.06%, 93.82%, and 97.53%, respectively. These results suggest the importance of health promotion within university settings, together with mental health strategies focused on adaptive psychological functioning, to prevent the risk of burnout.
由于繁重的学业负担和成绩期望，大学生出现倦怠和心理困扰的风险增加。本研究的目的是分析心理和生活方式变量与学业倦怠之间的关系，并确定心理学专业学生的倦怠风险特征。本研究采用横断面设计，纳入了274名葡萄牙心理学专业学生，其中大多数是本科生（72.6%）。对参与者的心理健康、心理困扰、情绪调节困难、饮食类型、体育活动、睡眠质量和倦怠情况进行了评估。结果表明，心理困扰、情绪调节困难和睡眠质量与倦怠呈正相关，而心理健康与倦怠呈负相关。使用机器学习算法，发现了两种不同的特征：“倦怠风险”和“无风险”。共有62名参与者被确定属于倦怠风险特征，表现出更高水平的困扰、情绪调节困难、较差的心理健康和睡眠质量、促炎饮食以及较少的体育活动。三种机器学习模型——随机森林、极端梯度提升和支持向量机——的准确率分别为95.06%、93.82%和97.53%。这些结果表明在大学环境中促进健康以及关注适应性心理功能的心理健康策略对于预防倦怠风险的重要性。
