Research Project 1

摘要/摘要--项目1 儿童和青少年虐待预防因果数据科学中心项目1 (冠军中心)将涉及确定多个类别的预测和因果模型 虐待暴露及其后果，并制定一套可扩展的决策 这些模型中的支持工具(DST)。这些DST将设计为通过以下方式指导决策 有能力预防虐待并减轻与虐待有关的严重程度的从业者 结果。鉴于对非因果/非病因目标的干预不能 改善健康或适应功能，这样的决定要求从业者能够 自信地：1)确定导致健康或功能损害的病因，2)针对 与特定儿童和家庭的这些损害相关的外部或内部病因因素。 当病因很复杂，干预措施必须是 定制以精确地针对不同的原因集，这通常适用于以下儿童和家庭 符合与虐待相关的干预标准。项目1的目标是确定模型和 开发一套指导此类决策的DST。我们将通过应用高级因果数据来实现这一点 对几个大型相关数据集的科学方法，以推导出几个有效的因果模型 暴露的虐待类别(性虐待、身体虐待、忽视)和虐待-- 相关结果(创伤后应激障碍、抑郁、自残、攻击性、物质 虐待、肥胖、功能障碍)。我们将把这些分析与机器学习结合起来 预测分类，以确定哪些预测模型可以准确地将儿童归类为风险 类别。我们将与资源核心的成员密切合作，开发模型和 传播和外联核心，以吸引所有相关利益攸关方(领域 专家、初级保健儿科医生、儿童福利工作者、行为健康临床医生、机构 管理员、孩子和家庭成员)，以确保Project 1开发的工具 有效、实用、值得信赖、安全。项目1开发的DST中的一个将构成基础 对于项目2，它将在现场试验中测试该工具。来自项目2的见解将是项目的关键 1对其开发的DST进行迭代改进。

SUMMARY/ABSTRACT – PROJECT 1 Project 1 of the Center on Causal Data Science for Child and Adolescent Maltreatment Prevention (CHAMP Center) will involve determining predictive and causal models for many categories of maltreatment exposures and their consequential outcomes, and developing a scalable set of decision support tools (DSTs) from these models. These DSTs will be designed to guide decisions by practitioners in a position to prevent maltreatment and mitigate the severity of maltreatment-related outcomes. Given that intervention on targets that are non-causal/non-etiological factors cannot improve health or adaptive functioning, such decisions require that that the practitioner can confidently: 1) identify etiological factors that cause health or functional impairments, and 2) target the external or internal etiological factors relevant for these impairments for a particular child and family. This becomes especially challenging when etiologies are complex and interventions must be customized to precisely target diverse sets of causes, as is often true for children and families who meet criteria for maltreatment-related interventions. The goal of Project 1 is to determine models and to develop a set of DSTs to guide such decisions. We will do this by applying advanced causal data science methods to several large, relevant data sets to derive valid causal models for several categories of maltreatment exposure (sexual abuse, physical abuse, neglect) and maltreatment- related outcomes (post-traumatic stress disorder, depression, self-harm, aggression, substance abuse, obesity, functional impairment). We will integrate these analyses with machine learning predictive classification to identify which predictive models can accurately classify children into risk categories. We will work closely with members of the Resource Core to develop the models and of the Dissemination & Outreach Core to engage the full spectrum of relevant stakeholders (domain experts, primary care pediatricians, child welfare workers, behavioral health clinicians, agency administrators, children, and family members) to ensure that the tools Project 1 develops are effective, practical, trustworthy, and safe. One of the DSTs that Project 1 develops will form the basis for Project 2, which will test this tool in a field trial. Insights from Project 2 will be essential for Project 1 to iteratively improve on the DSTs it develops.