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预测中国大学生艾滋病毒自我检测意图:一个双模型分析,整合健康信念结构和机器学习优先级

Yan Jiang¹, Jing Li^1,2, Jingfen Lu³

¹School of Public Health, Xiangnan University, Chenzhou, China.

Frontiers in public health

|August 18, 2025

概括

中国的大学生表现出对艾滋病毒自我检测 (HIVST) 的高度意愿. 关键因素包括自我效能和行动的线索,以及阻碍吸收的感知障碍. 建议针对男性和艺术学生进行有针对性的干预.

科学领域:

公共卫生公共卫生
行为科学行为科学
流行病学流行病学

背景情况:

在中国,大学生是易受艾滋病毒感染的关键人群.
艾滋病毒自我检测 (HIVST) 是提高检测和及时干预的关键策略.
有限的研究整合了行为理论和机器学习,以了解这一人口中的HIVST意愿.

研究的目的:

探索中国大学生对艾滋病毒自检意愿的决定因素.
在本次调查中应用健康信念模型 (HBM) 和随机森林分析.

主要方法:

一项涉及1015名来自南学院的本科生 (2022年7月至8月) 的横截面研究.
在招募参与者时,采用了分层集群抽样.
通过问卷收集的数据,使用逻辑回归和基于HBM的随机森林建模来分析.

主要成果:

69.3%的参与者表示愿意在六个月内接受HIVST.
自我有效性和行动线索显著促进了HIVST的意愿 (OR > 1),而感知到的障碍则抑制了它 (OR < 1).
随机森林分析确定男性学生和艺术专业是需要有针对性的干预的关键亚群.

结论:

校园艾滋病毒预防需要性别特定的计划,HBM知情教育来提高自我效能和线索,以及结构性干预来降低测试障碍.
整合理论和实证方法可以提高对HIVST行为的预测性理解.
调查结果为在学术环境中制定精确的公共卫生战略提供了可操作的见解.

关键词:

艾滋病毒自我测试艾滋病毒自我测试大学生学生的大学生健康信仰模型的模型.高风险行为高风险行为.随机森林建模随机森林建模

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