AUTHOR=Agudamu  , Li Yingying , Mi Na , Pan Xinyue 

TITLE=A computational grounded theory based analysis of research on China’s old-age social welfare system

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1556302

DOI=10.3389/fpubh.2025.1556302

ISSN=2296-2565

ABSTRACT=PurposeBy the end of 2024, 22% of the Chinese population was aged 60 and above, making old-age social welfare a critical challenge. Despite abundant literature, a gap remains between research and policy. This study applies Nelson’s computational grounded theory to systematically analyze China’s old-age social welfare research and propose targeted policy priorities.MethodsWe searched Chinese literature (2014–2024) from the Wanfang, CNKI, and CQVIP databases. After preprocessing the abstracts, we applied topic modeling using the latent Dirichlet allocation, guided by human analysts. Optimal topics were determined using perplexity and coherence metrics. Researchers then linked each topic to sociologically meaningful concepts to derive abstract policy conclusions.ResultsA total of 413 articles met eligibility criteria. Seven topics emerged: (1) the theoretical significance of social welfare policy; (2) enhancing rural old-age care; (3) providing care for special groups; (4) promoting a home-community care model; (5) optimizing precision care through collaborative mechanisms; (6) developing community culture; and (7) establishing supply-driven care services. Notably, topics two and seven dominated the literature.ConclusionBased on these themes, we propose policy priorities to enhance comprehensive social welfare programs. China’s big government model—a top-level design involving diverse stakeholders—may serve as an effective framework for addressing a global aging society marked by rising non-communicable diseases and AI-driven economic growth. Moreover, our computer-assisted approach offers a valuable method for information scientists, aiding policymakers in navigating extensive digital data for more cost-effective and timely decision-making.