Alignment Between China’s Elderly Care Policies and the Integrated Theory of Social Gerontology: A Text Analysis from 2000 to 2025
Against the backdrop of global aging, whether the diverse content of elderly care policies can be systematically analyzed and interpreted through a unified theoretical framework remains an open question in gerontology. This study analyzes 2508 elderly care policy documents issued in China from 2000 to 2025 to assess the alignment degree between elderly care policies and the Integrated Theory of Social Gerontology, utilizing Latent Dirichlet Allocation (LDA) topic modeling and a text content alignment degree model constructed based on a normalized co-occurrence algorithm. The findings reveal that all 11 topics extracted from Chinese elderly care policies correspond to the six dimensions of the Integrated Theory of Social Gerontology, with an overall alignment coefficient of 0.689, indicating moderate alignment. This threshold is defined based on domain-specific benchmarks: alignment coefficients ≥0.75 are classified as ‘high alignment’, 0.5–0.74 as ‘moderate alignment’, and <0.5 as ‘low alignment’, consistent with quantitative standards for policy-theory alignment research in gerontology. Four dimensions (public support systems, individual physical/psychological health, economic security, family-cultural traditions) show high alignment, while two (social stratification, historical context) exhibit low alignment, reflecting significant policy coverage asymmetries. Methodologically, this study develops a replicable policy theory alignment model, filling gaps in integrated gerontology policy analytical tools. Empirically, it provides the first large-scale longitudinal analysis of Chinese elderly care policies, illuminating policy design’s theoretical foundations and gaps in structural/historical dimension coverage.
Authors
- Li Wan (ORCID: https://orcid.org/0000-0003-0865-0967)
- Yang Weihua
- Yu Ren
Institutions
- Dalian Polytechnic University (CN)
Publication Details
- Journal
- Sustainability
- Published
- 2026-02-16
- DOI
- https://doi.org/10.3390/su18042017
- Primary Topic
- Computational and Text Analysis Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00