aiqbench analysis
This article introduces CCQ, a large-scale dataset designed to facilitate AI research into early childhood health by integrating fragmented child care records across 12 U.S. states. The authors utilize an LLM-based pipeline for data curation and benchmark various machine learning models, finding that tabular classifiers outperform language models within single states while cross-state transfer remains challenging without supervision. This work directly addresses the scarcity of high-quality, privacy-compliant datasets needed to advance applied AI in social determinants of health.
CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research
arXiv:2610.05863v1 Announce Type: cross Abstract: 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.
Abstract: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.
29 pages, 8 figures, 7 tables. Dataset: this https URL ; Code: this https URL
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.05863 [cs.LG]
(or arXiv:2610.05863v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.05863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 301 words
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