Evidence map›Paper›PMID 41462239›Full record

ArticleBMC health services research2025

Balancing quality collaborative supervision and tripartite satisfaction in elderly healthcare public-private partnerships: an evolutionary game and simulation analysis.

Xianghua Yue, Shahzad Khan Durrani, Runfa Li

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xianghua YueSchool of Economics and Management, Xiangnan University, Chenzhou, 423000, PR China.
Shahzad Khan DurraniSchool of Economics, Beijing Institute of Technology, Beijing, 100081, PR China. shahzaddurrani@hotmail.com.
Runfa LiBusiness School, Guangzhou College of Business and Technology, Guangzhou, 510850, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China’s rapidly growing elderly population, shifting social patterns, and economic developments have significantly impacted traditional family-based caregiving, leading to the widespread adoption of Public-Private Partnership (PPP) models for institutional care. Effective elderly care PPPs require quality collaborative supervision among the government, the private sector, and the public to ensure tripartite satisfaction and promote social well-being. However,challenges such as service quality failures and disputes in several regional elderly care PPP projects stem from insufficient government supervision and limited public participation, leading to information asymmetry, incomplete contracts, and suboptimal service quality. Existing research lacks comprehensive models that address the public’s role as a key stakeholder and the dynamic interplay among critical variables affecting PPP stability. This study uses evolutionary game theory (EGT) to explore the strategic interactions between the government, private sector, and public in elderly care PPPs. The model incorporates key factors such as reputation, damage compensation, and government intervention to enhance public participation and tripartite satisfaction. Findings reveal that (1) high-quality services, active public participation, and efficient government supervision are crucial for effective collaborative supervision. (2) Stakeholders’ initial willingness to cooperate, compensation for public interest damages, reputation, and punitive measures are key determinants for stability. (3) Compensation for public interest damages and guaranteed incomes significantly enhance public participation in the supervision process. MATLAB simulations validate the model’s stability, specifically, the simulation shows that the system evolves effectively toward the ideal state when public damage compensation exceeds participation costs and penalties for low-quality services outweigh the cost savings from providing them. Based on these findings, we propose targeted strategic measures such as calibrating compensation and penalty levels based on these threshold relationships to improve collaborative quality and ensure satisfaction among all stakeholders in elderly healthcare PPPs in China, with broader applicability to other sectors and regions.

Indexed as

Health Services for the AgedPublic-Private Sector PartnershipsQuality of Health CareChinaGame TheoryHumansCollaborative quality controlElderly care industryEvolutionary gamePPP projectTripartite satisfaction

Identifiers

PMID41462239
PMCPMC12866519

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