Evidence map›Paper›PMID 41006585›Full record

ArticleScientific reports2025

Applying the SFIC synergistic governance model to analyze determinants of multisectoral collaborative chronic disease management in Beijing's district H.

Jin Li, Ning Zhao, Mei Gu, Jia Yang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jin LiHospital Office, Shenzhen Traditional Chinese Medicine Hospital, Guangdong, China.
Ning ZhaoMedical Insurance and Price Administration Office, Women's Hospital, School of Medicine, Zhejiang University, Zhejiang, China.
Mei GuSchool of Public Health, Capital Medical University, No. 10, You-an-men Wai, Xi-tou-tiao, Feng-tai District, Beijing, 100069, China.
Jia YangSchool of Public Health, Capital Medical University, No. 10, You-an-men Wai, Xi-tou-tiao, Feng-tai District, Beijing, 100069, China. yangjiakm@163.com.

Funding

Beijing Municipal Commission of Education SZ201910025008
6 · The paper itself

Abstract

The prevalence of chronic diseases is influenced by multiple factors, making their management a complex social and multi-sectoral issue. China has implemented a multisectoral synergistic model for managing chronic diseases; however, enhancing its synergistic effectiveness remains necessary. This study aims to investigate the factors influencing multi-sectoral synergy in chronic disease management. We employed purposive sampling and multi-stage stratified sampling method to survey 160 personnel involved in chronic disease management across 10 departments of county-township-village three level in H District, Beijing. The SFIC (Starting Conditions, Facilitative Leadership, Institutional Design, and Collaborative Process) model served as the framework for the study, and structural equation modeling was conducted using AMOS 24.0 software to analyze the factors influencing the effectiveness of multisectoral collaboration in chronic disease management. The total effects of starting conditions, facilitative leadership, institutional design, and collaborative processes on the effectiveness of multi-sectoral collaboration in chronic disease management were 0.370, 0.638, 0.657, and 0.380, respectively. Among these factors, institutional design had the greatest impact on synergistic effectiveness (0.657). Additionally, the collaborative processes exhibited a mediating effect, contributing 0.525 to the total mediated effect. Starting conditions, facilitative leadership, institutional design, and collaborative processes all influence the effectiveness of multi-sectoral collaboration in chronic disease management, with the collaborative processes mediating these effects. Further efforts should focus on refining institutional design and facilitative leadership, while also considering the influence of the collaborative processes on synergistic effects.

Indexed as

Disease ManagementBeijingChinaChronic DiseaseCooperative BehaviorFemaleHumansLeadershipMaleSurveys and QuestionnairesChronic disease managementMulti-sectoral collaborationSFIC modelStructural equation modelSynergistic governance

Identifiers

PMID41006585
PMCPMC12475240

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LicenceCC BY-NC-ND
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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.