Transforming aged care in China: insights from a TF-IDF-based data mining analysis of national policies (2018–2022) - Zhihan Liu; Hebin Li; Ziyan Zhang; Lu Ping; Wenxin Gu; Yuan Yao (BMC Geriatrics)
Abstract Background As China grapples with rapid demographic shifts due to an aging population, the urgency for innovative aged care policies has intensified. Objective This study addresses a significant research gap by employing a text mining approach based on the Term Frequency-Inverse Document Frequency (TF-IDF) method, quantitatively analyzing a broad array of policy documents from 2018 to 2022. Methods It identifies key trends and shifts, using core feature words to construct co-occurrence and heterogeneity matrices analyzed via multidimensional scaling techniques. Results Research results from 2018 to 2022 highlight China’s focus on epidemic prevention, integrated care tailored to Chinese contexts, innovative social security and financial products, and the advancement of smart aged care against a backdrop of multifaceted demands. By analyzing policy changes surrounding significant political events and conducting comparative analysis with previous studies, this study delineates the evolution of policy priorities and anticipates future directions, highlighting the need to integrate advanced technologies and financial mechanisms to strengthen service delivery systems. These adjustments not only meet current demands but also strategically position China to navigate the complexities of an aging society. Conclusions This research provides critical insights for global policymakers facing similar demographic challenges, advocating for a resilient, holistic, and inclusive aged care system.