管理学生抑郁症的新数字方法,重点是通过学术压力分析通过早期检测压力分析
Anupam Yadav1, Muhamed Husseyn2,3, Shreenidhi H S4
1Department of Computer Engineering and Application, GLA University, Mathura, India.
Informatics for health & social care
|January 6, 2026
概括
学术压力显著影响学生的心理健康,导致抑郁症状. 这项研究开发了一种使用预测模型的数字工具,以准确识别风险较高的学生进行早期干预.
科学领域:
- 教育心理学教育心理学
- 数字健康数字健康
- 心理健康研究 心理健康研究
背景情况:
- 学生的心理健康是一个日益关注的问题,学术压力加剧了抑郁症状.
- 现有系统在早期发现和积极干预学生抑郁症方面面临挑战.
- 需要可扩展的,数据驱动的工具来支持教育和医疗保健系统.
研究的目的:
- 引入一种新的数字战略,用于早期发现学生抑郁症.
- 分析学术压力因素与心理健康结果之间的关系.
- 开发准确的预测模型来识别有风险的学生.
主要方法:
- 分析了5000个匿名的学生记录,包括心理,行为,学术和人口统计数据.
- 在教育背景下确定抑郁症状的关键驱动因素.
- 开发和验证用于风险评估的预测模型 (达到0.969准确度).
主要成果:
- 学术压力被认为是导致抑郁症状的最重要因素.
- 根据关键驱动因素,为学生创建了一个有针对性的风险概况.
- 开发了高准确度的预测模型,以识别有风险的个人.
结论:
- 数字战略是学校和医疗保健提供者有效的决策支持工具.
- 这种方法提高了主动心理健康支持的能力,并减少了查负担.
- 通过数据分析进行早期检测,可以改善患有抑郁症的学生获得及时支持的机会.
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