与心理健康问题有关的研究经验和职业因素:患病率,风险因素和基于机器学习的预测估计
Mohammed A Mamun1,2,3, Md Abu Huraira1,4, Momotaj Begum1,5
1CHINTA Research Bangladesh, Savar, Dhaka, Bangladesh.
The International journal of social psychiatry
|August 16, 2025
概括
研究参与显著影响学生的心理健康,经验和职业兴趣预测抑郁症,焦虑和失眠. 机器学习可以识别有风险的学生,以提供有针对性的支持.
科学领域:
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 抑郁症,焦虑和失眠等心理健康问题在大学生和毕业生中不断增加.
- 研究活动对学生心理健康的影响尚不清楚.
研究的目的:
- 调查研究相关因素与大学生和毕业生的心理健康之间的联系.
- 为了本次考试,利用机器学习,统计分析和GIS映射.
主要方法:
- 收集了来自508名学生/毕业生的人口统计,学者,研究参与和心理健康方面的数据.
- 使用统计分析 (SPSS),空间分析 (QGIS) 和机器学习 (Python).
主要成果:
- 抑郁症 (39.8%),焦虑症 (29.3%) 和失眠症 (12.2%) 的高患病率被观察到.
- 研究经验,具体的课程和职业兴趣是心理健康结果的关键预测因素.
- 机器学习模型 (CatBoost,SVM,Random Forest) 在预测心理健康状况方面表现出有效性;GIS显示没有显著的区域差异.
结论:
- 机器学习可以帮助机构识别有风险的学生,以提供个性化的心理健康支持.
- 培养支持性的研究环境可以改善学生的心理健康和学术成功.
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