公平意识的K意味着对高等教育学生的数字心理健康进行聚类:为公平的聚类提供一个可概括的框架
Priyanshu Alluri1, Zequn Chen2, Thomas Thesen3,4,5
1Dartmouth College, Hanover, NH 03755, United States.
JAMIA open
|January 14, 2026
概括
一个新的社会公平的集群框架将在种族群体之间减少30%的心理健康风险分层差异. 这种方法确保了公平的集群成本,并确定了影响学生福祉和恢复能力的关键因素.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
- 心理健康研究 心理健康研究
背景情况:
- 高等教育学生面临着重大的心理健康挑战,包括焦虑,抑郁和倦怠,不成比例地影响到代表性不足的群体.
- 像K-means集群这样的临床信息学工具可以分层心理健康风险,但可能会加剧现有的差异.
研究的目的:
- 开发和评估一个社会公平的集群框架,用于高等教育中的心理健康风险分层.
- 确保跨人口群体的集群成本公平,同时尽量减少集群内部的变化.
主要方法:
- 对比标准K-意味着与社会公平的变体进行集群,以评估人口差异影响.
- 利用综合和特此后的统计测试来识别影响人口统计集群的因素.
- 量化了重要因素对集群发展的影响,使用了健康思维网络调查数据.
主要成果:
- 社会公平的集群方法在种族群体之间将集群成本的差异降低了高达30%.
- 歧视经历强烈预测心理健康状况较差;财务稳定和社会参与促进了弹性.
- 公平意识的集群提供了与标准模型相比更好地反映差异的分区.
结论:
- 将公平性约束集成到集群算法中可以提高风险分层和分区系统中的公平性.
- 该框架为学生心理健康的社会经济驱动因素提供了有价值的见解.
- 这种方法广泛适用于各种生物医学信息学集群问题.
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