通过全司法管辖区的儿童虐待预防工作,调整家庭级服务阵列:地理空间和反事实建模研究协议
Jamaal Green1, Brian Glass2, Jordan Purdy2
1University of Pennsylvania, 127 Meyerson Hall, 210 S. 34th Street, Philadelphia, PA, 19104, United States, 1 3018013109.
JMIR research protocols
|December 30, 2025
概括
本研究使用地理空间平滑和反事实建模来识别儿童虐待的高风险区域,并优化服务分配. 目标是通过有效地将资源分配给有需要的家庭来改善儿童福利成果.
科学领域:
- 公共卫生 公共卫生
- 地理空间分析的研究.
- 机器学习 机器学习
背景情况:
- 虐待儿童会导致严重的个人和社会负面结果.
- 遭受虐待的儿童面临心理障碍,吸毒,犯罪,自杀和教育问题的风险增加.
研究的目的:
- 通过优化服务分配来预防和改善儿童虐待.
- 使用地理空间平滑和风险地形建模 (RTM) 识别高风险区域.
- 通过反事实建模来确定最佳的服务重新分配,以改善结果.
主要方法:
- 一个三阶段的代过程,结合了RTM和反事实建模.
- 基于历史数据和建筑环境因素,RTM使用机器学习绘制虐待风险图.
- 反事实模型优化了家庭级别的服务分配,考虑到约束和公平性.
主要成果:
- 目前正在招募参与司法管辖区,以便在2025年9月之前实施该研究.
- 该方法允许在不同司法管辖区灵活应用.
结论:
- 这种新的方法优化了对儿童虐待风险家庭的支持服务分配.
- 它将基于数据的策略与基于结果的反事实建模相结合,以提高儿童福利.
- 灵活的方法可以适应各种司法偏好和约束.
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