CCQ：一个支持儿童健康研究人工智能的多州儿童保育质量数据集
CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research
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中文总结 AI 辅助
本文提出CCQ，一个涵盖美国12州59,372条记录的大规模去标识化儿童保育质量数据集，通过LLM自动化策展流程生成文本和表格两个版本，并基准测试多种模型，旨在推动AI在儿童健康研究中的应用。
中文摘要 AI 辅助
高质量的早期儿童保育是儿童成长和发展的关键决定因素。关于儿童保育质量的研究一直受到碎片化、非研究友好且受隐私约束的数据集的限制。我们提出了CCQ（儿童保育质量），这是一个大规模、去标识化的数据集，用于人工智能与早期儿童健康交叉领域的应用数据科学研究。CCQ整合了美国12个州的59,372条儿童保育提供者记录，涵盖了多样化的提供者类型以及数据模式。为确保研究实用性同时保护隐私，我们实现了一个基于LLM的自动化策展流程，该流程对原始州记录进行匿名化、清洗和标准化，并生成两个互补的发布版本：一个清洗后的文本版本和一个完全预处理的表格版本。我们还在质量评级预测和重要特征分析上对传统机器学习模型、表格基础模型和语言模型进行了基准测试。在州内，预处理表格上的表格分类器表现最佳。跨州时，零样本迁移接近随机水平，但适度的目标州监督恢复了大部分州内性能，且在其他州上的预训练有益于微调的语言模型。我们发布了两个数据集及所有代码，以加速人工智能驱动的儿童保育质量研究，并最终改善儿童的健康和发展。
英文摘要
High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constrained by fragmented, non-research-friendly, and privacy-bound datasets. We present CCQ (Child Care Quality), a large-scale, de-identified dataset for applied data science research at the intersection of AI and early childhood health. CCQ integrates 59,372 child care provider records across 12 U.S. states, covering diverse provider types as well as data schemas. To ensure research utility while protecting privacy, we implement an automated, LLM-based curation pipeline that anonymizes, cleans, and standardizes raw state records into two complementary releases: a cleaned textual release and a fully preprocessed tabular release. We also benchmark traditional machine learning models, tabular foundation models, and language models on quality rating prediction and important features analytics. Within a state, tabular classifiers on the preprocessed tables perform best. Across states, zero-shot transfer is near chance, but modest target-state supervision recovers most of the within-state performance, and pretraining on other states benefits finetuned language models. We release both datasets with all code to accelerate AI-driven research on child care quality and ultimately improve children's health and development.
发表机构
- Emory University（埃默里大学）
机构由 AI 辅助整理，请以论文原文为准。
