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使用机器学习方法确定青少年心理和身体健康的最佳预测因素

Yadi Sun¹, Ying Lin², Henry C Y Ho¹

¹The Education University of Hong Kong, Hong Kong.

Journal of affective disorders

|December 26, 2025

概括

青少年的精神和身体健康受到情感和社会资本的影响. 干预措施应考虑文化特点,以制定有效的青少年健康战略.

科学领域:

青少年健康青少年健康
心理健康心理健康
身体健康身体健康
跨文化心理学跨文化心理学
机器学习应用机器学习应用

背景情况:

青少年健康是一个全球性的问题,先前的研究往往只关注心理和身体健康问题的有限预测领域.
现有研究经常检查单个或少数预测域,可能忽视影响青少年幸福感的因素的复杂相互作用.

研究的目的:

通过使用先进的分析技术,确定青少年心理和身体健康的最佳预测因素.
检查一组全面的46个预测因素在9个领域对青少年健康结果的影响.
为了比较不同文化背景的预测因素的重要性,特别是香港和荷兰.

主要方法:

利用多个机器学习算法来分析大量青少年健康指标的数据集.
来自国际学生评估计划 (PISA) 的2022年就业数据,包括来自香港和荷兰的青少年.
评估了九个领域的46个变量的心理和身体健康预测能力.

主要成果:

情绪和社会资本领域在研究的两种社会中都成为青少年精神和身体健康的最重要的预测因素.
识别了文化细微的模式:环境领域在香港至关重要,物理领域在荷兰至关重要.
证明在一个社会的数据上训练的预测模型在另一个社会的数据上表现不那么有效,突出了文化特异性.

结论:

专注于增强情感和社会资本的干预措施有望改善青少年的心理和身体健康.
强调了文化定制干预策略的必要性,承认重叠的模式和独特的社会影响.
建议对青少年健康干预采取细微的方法,尊重文化多样性,同时利用共同的预测因素.

关键词:

青少年健康青少年健康跨社会模式的跨社会模式机器学习机器学习最好的预测因素是最好的预测因素.在 PISA 测试中,

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