ArticleBMC public health2025
Mapping the interactions of critical factors in China's first-contact healthcare systems using causal loop diagrams.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Why do people trust (or mistrust) health organizations? A systematic review.International journal for equity in health · 2026Pooled it
- Resource allocation efficiency in Ethiopian hospitals: technology utilization as mediator and staff competency as moderator.Cost effectiveness and resource allocation : C/E · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundPrimary healthcare plays a pivotal role in optimizing healthcare resource allocation and ensuring equitable access to services. In China, first-contact care within the tiered medical care system is essential for alleviating pressure on large hospitals and enhancing healthcare equity. However, challenges such as uneven resource distribution and low patient trust in primary institutions limit the effectiveness of first-contact care. This study aims to explore the dynamic interactions of key factors influencing first-contact effectiveness and provide evidence-based policy recommendations.
methodsA systematic approach was employed, integrating a literature review, qualitative interviews, and MICMAC structural analysis. Using Causal Loop Diagram (CLD) methodology, this study mapped the causal pathways and feedback mechanisms among factors such as health insurance awareness, medical consortium development, and tiered healthcare policy guidance. MICMAC analysis quantified the influence and dependence of these variables to identify key drivers within the system.
resultsThe study revealed that primary healthcare service quality, patient trust, and healthcare-seeking behaviors are are vital determinants for improving first-contact effectiveness. Key driving factors include health insurance awareness, medical consortium construction, and tiered healthcare policy guidance, which form reinforcing feedback loops to enhance system efficiency. The findings highlight the importance of resource sharing, trust-building, and policy support in optimizing the tiered medical care system.
conclusionsThis study provides a comprehensive framework for understanding the complexities of first-contact care within China's healthcare system. The insights gained emphasize the need for targeted interventions, including promoting health insurance awareness, strengthening medical consortiums, and improving policy incentives. These recommendations can inform policy reforms aimed at achieving equitable and efficient healthcare systems globally.
trial registrationNot applicable, as this study does not involve a healthcare intervention on human participants.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.