个人和背景因素对青少年问题互联网使用的双重影响:机器学习方法和SHAP解释
Ke Huang1,2,3, Yuyao Yang1,2,3, Linxin Wang4
11School of Psychology, Shenzhen University, Shenzhen, China.
Journal of behavioral addictions
|March 10, 2026
概括
青少年 青少年 青少年
科学领域:
- 心理学 心理学 心理学
- 青少年发展 青少年发展
- 数字健康数字健康
背景情况:
- 问题性互联网使用 (PIU) 是青少年日益关注的问题.
- 了解个人和背景因素对于有效的干预至关重要.
研究的目的:
- 通过机器学习,确定青少年PIU的关键个体和上下文相关性.
- 探索这些因素之间的相互作用.
主要方法:
- 将I-PACE模型和RDS理论应用于一个大数据集 (N=68,425名青少年).
- 使用了五种集体机器学习模型 (AdaBoost,随机森林,LightGBM,Bagging,CatBoost) 进行嵌套交叉验证.
- 采用了SHapley添加式解释 (SHAP) 来进行特征识别和对相互作用的双变量部分依赖分析.
主要成果:
- PIU风险的流行率为23.2%.
- 对不确定性的不容忍 (个人) 和周末视频游戏时间 (行为) 是关键的风险因素;正念是关键的保护因素.
- 离开家的意图 (家庭) 和欺凌行为 (同龄人) 是显著的背景风险. 发现了显著的个人内部和个人背景相互作用.
结论:
- 风险因素在个人和情境系统中积累,影响青少年的适应能力.
- 意识表现出跨系统的保护作用,表明它在PIU的有针对性的干预中具有潜力.
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