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Background <p>Child poverty adversely affects children's health and social lives. Children in households receiving public assistance have diverse needs regarding their health and lives Therefore, they require tailored support interventions based on individual sociodemographic backgrounds and other personal attributes. However, to the best of our knowledge, no study has identified distinct segments of these children using self-reported data or evaluated the health and social support strategies for the obtained segments. This study aimed to apply a clustering technique to quantitatively identify the distinct segments among children in households receiving public assistance, qualitatively describe the segment characteristics, and suggest potentially effective health and social life support strategies for each segment.</p> Methods <p>In the quantitative phase, we used the probabilistic latent semantic analysis (PLSA), a machine learning-based soft clustering method, to identify the segments of children aged 10–15 years in households receiving public assistance. We used the segments obtained in the quantitative phase to subsequently conduct seven semi-structured interviews and one focus group with professionals supporting children with complex needs. The interviewees were asked to describe the segment characteristics and propose health and social life support strategies for each segment.</p> Results <p>In total, 1,275 children were included in the quantitative analysis. PLSA identified all the segments with distinct characteristics that made sense to professionals who supported children in households receiving public assistance, confirmed by qualitative findings: e.g. “children who can do things on their own” (Segment 1), “children living in a facility” (Segment 2), “children who are in <i>hikikomori</i> (social withdrawal)” (Segment 3), “children who cannot be bothered to answer conceptual questions about themselves” (Segment 4), and “children from households with intergenerational public assistance use” (Segment 5). The qualitative findings also suggested various support measures not only for physical health but also for the social and mental health of children in households receiving public assistance, for example, “financial support for higher education” for Segment 1, and “whole family support” for Segment 5.</p> Conclusions <p>Children in households receiving public assistance can be categorized into distinct segments and would consequently benefit from tailored support strategies for their health and social lives.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月16日

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  • Keiko Ueno,
  • Daisuke Nishioka,
  • Shiho Kino,
  • Naoki Kondo

摘要

Background

Child poverty adversely affects children's health and social lives. Children in households receiving public assistance have diverse needs regarding their health and lives Therefore, they require tailored support interventions based on individual sociodemographic backgrounds and other personal attributes. However, to the best of our knowledge, no study has identified distinct segments of these children using self-reported data or evaluated the health and social support strategies for the obtained segments. This study aimed to apply a clustering technique to quantitatively identify the distinct segments among children in households receiving public assistance, qualitatively describe the segment characteristics, and suggest potentially effective health and social life support strategies for each segment.

Methods

In the quantitative phase, we used the probabilistic latent semantic analysis (PLSA), a machine learning-based soft clustering method, to identify the segments of children aged 10–15 years in households receiving public assistance. We used the segments obtained in the quantitative phase to subsequently conduct seven semi-structured interviews and one focus group with professionals supporting children with complex needs. The interviewees were asked to describe the segment characteristics and propose health and social life support strategies for each segment.

Results

In total, 1,275 children were included in the quantitative analysis. PLSA identified all the segments with distinct characteristics that made sense to professionals who supported children in households receiving public assistance, confirmed by qualitative findings: e.g. “children who can do things on their own” (Segment 1), “children living in a facility” (Segment 2), “children who are in hikikomori (social withdrawal)” (Segment 3), “children who cannot be bothered to answer conceptual questions about themselves” (Segment 4), and “children from households with intergenerational public assistance use” (Segment 5). The qualitative findings also suggested various support measures not only for physical health but also for the social and mental health of children in households receiving public assistance, for example, “financial support for higher education” for Segment 1, and “whole family support” for Segment 5.

Conclusions

Children in households receiving public assistance can be categorized into distinct segments and would consequently benefit from tailored support strategies for their health and social lives.