心理学生的倦怠风险概况:使用机器学习的探索性研究
M Graça Pereira1, Martim Santos1, Renata Magalhães2
1Psychology Research Centre (CIPsi), School of Psychology, University of Minho, 4710-057 Braga, Portugal.
Behavioral sciences (Basel, Switzerland)
|April 26, 2025
概括
大学学生面临着倦怠的风险. 这项研究确定了与心理学学生学术倦怠相关的关键心理和生活方式因素,使风险分析和预防策略成为可能.
科学领域:
- 心理学 心理学 心理学
- 公共卫生 公共卫生
- 教育心理学教育心理学
背景情况:
- 大学学生经历了高的学术工作量和绩效期望,增加了燃烧和心理痛苦的风险.
- 学术倦怠是高等教育的一个重大问题,影响学生的福祉和学术成功.
研究的目的:
- 分析心理和生活方式变量与葡萄牙心理学学生学术倦怠之间的关系.
- 在使用机器学习的心理学学生中识别明显的倦怠风险概况.
主要方法:
- 一项涉及274名葡萄牙心理学学生 (72.6%为本科生) 的横截面研究.
- 评估心理健康,痛苦,情绪调节,饮食,体育活动,睡眠质量和倦怠.
- 应用机器学习算法 (随机森林,XGBoost,SVM) 来识别倦怠风险概况.
主要成果:
- 心理困扰,情绪调节困难和睡眠质量差与倦怠积极相关.
- 心理健康与倦怠负面相关.
- 确定了两个配置文件:"倦怠风险" (62名参与者) 和"没有风险". "倦怠风险"个人资料显示了更高的痛苦,情绪困难,更低的幸福感和睡眠质量,促炎症饮食和更少的体力活动. 机器学习模型实现了高精度 (93.82%97.53%).
结论:
- 心理压力,情绪调节和睡眠质量是导致学生倦怠的关键因素.
- 机器学习有效地识别了大学生中明显的倦怠风险概况.
- 健康促进和有针对性的心理健康策略对于防止大学环境中的学术倦怠至关重要.
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