美国儿童福利系统中父子接触的最重要的预测因素:一种机器学习方法
Joyce Y Lee1, Garrett T Pace2, Keunhye Park3
1The Ohio State University College of Social Work, United States of America.
Child abuse & neglect
|December 30, 2025
概括
父亲参与儿童福利受到多种因素的影响,包括父亲的人口统计,社会工作者特征和家庭状况. 机器学习确定了各种系统级别的父子接触的关键预测因素.
科学领域:
- 儿童福利研究 儿童福利研究
- 数据科学在社会工作中的应用
- 生态系统理论 生态系统理论
背景情况:
- 对影响父亲参与美国儿童福利系统的多系统因素的理解有限.
- 数据科学,特别是机器学习的潜力,以推进关于父亲参与的研究.
研究的目的:
- 将生态系统框架应用于国家儿童福利数据.
- 利用机器学习 (随机森林) 识别父子接触的关键预测因素,跨系统层面.
主要方法:
- 在国家儿童和青少年福祉调查第三队列 (N=2380) 中使用随机森林分析.
- 分析了使用124个多系统级预测器的变量重要性.
主要成果:
- 父亲的社会人口统计特征 (种族,教育) 是重要的预测因素.
- 案例工作者特征,服务推,家庭经济/组成因素和儿童特征也预测了父子接触.
结论:
- 多系统因素以复杂的相互作用来预测父子接触.
- 机器学习在预测准确性,数据处理和模拟儿童福利研究的复杂关系方面提供了优势.
- 机器学习可以揭示新的见解,以推进儿童福利实践和政策.
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