Poverty adversely affects children’s health and social lives. Children in households receiving public assistance often have diverse health and lifestyle needs that require individualized support tailored to their specific living backgrounds. Moreover, effective support methods vary depending on each child’s circumstances.
To address this issue, a research team led by Keiko Ueno, an assistant professor in the Department of Social Epidemiology at Kyoto University Graduate School of Medicine, utilized responses from a questionnaire survey of 1,275 children. Using a machine learning technique called soft clustering, they categorized the children into small groups (segments) based on differences in their living backgrounds. The researchers then conducted interviews with professionals (including NPO staff, child psychiatrists, public health nurses, and school counselors; hereafter referred to as “experts”) to understand each segment’s lifestyle characteristics and collect opinions on suitable health and life support strategies. As a result, five distinct and expert-validated segments were identified.From the expert interviews, diverse support strategies were suggested, addressing not only physical health but also social and mental well-being. Based on these findings, the team is now developing a tailor-made support system that presents support plans matched to each segment.
This research was published online in the International Journal for Equity in Health on April 16, 2025.
Article:Ueno K, Nishioka D, Shiho K, Naoki K. A data-driven approach to detect support strategies for children living in households receiving public assistance in Japan: a mixed methods study to establish tailor-made health and welfare care. Int J Equity Health. 2025;24:103.
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