A quantitative evaluation study of China’s Long-Term Care insurance policies based on the PMC index model: A case study of 16 pilot policy texts

China’s Long-Term Care Insurance (LTCI) system is designed to address the caregiving needs of elderly individuals with disabilities and dementia, focusing on long-term care to ensure adequate survival and quality of life for disabled individuals. This study employs the PMC index model to evaluate LTCI policies in 16 pilot cities, including Changchun and Qingdao, using keyword extraction and social network analysis on the latest policy documents from these cities. The study revealed the following findings: (1) Among the 16 cities, only Qingdao’s policy achieved a “perfect” rating, with two policies rated as “excellent,” 11 as “acceptable,” and two as “poor.” (2) While most cities allow the involvement of commercial insurance in LTCI administration, there is a lack of clear policy direction for the assessment and service provision by commercial insurers. (3) Dementia care receives significantly less attention compared to physical disabilities. (4) Most cities have underdeveloped financing mechanisms, and family caregiving services are undervalued. Moreover, an analysis of representative policies based on the PMC surface indicates substantial differences between the pilot cities, with Qingdao’s “perfect” policy serving as a model for future LTCI development. The study offers several recommendations: (1) Improve caregiver support policies to enhance family caregiving services. (2) Expand funding sources to increase the equity of LTCI financing. (3) Allocate insurance policy resources more effectively to gradually eliminate policy barriers. (4) Increase the focus on dementia care and clarify the criteria for assessing disability. (5) Strengthen the preventive function of LTCI and progressively expand its coverage. This research provides critical insights into the ongoing development of China’s LTCI system and proposes viable strategies for promoting equity and sustainable growth.