Evidence map›Paper›PMID 38249420›Full record

ArticleFrontiers in public health2023

Driving factors of urban community epidemic prevention and control capability: QCA analysis based on typical cases of 20 anti-epidemic communities in China.

Ruyi Shi, Bo Lu, Yiwen Zhong

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. 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.

Ruyi ShiSchool of Public Policy and Management, China University of Mining and Technology, Xuzhou, China.
Bo LuSchool of Public Policy and Management, China University of Mining and Technology, Xuzhou, China.
Yiwen ZhongChina North Industries Corporation, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In the wake of the COVID-19 outbreak, urban communities have emerged as the frontline defenders in epidemic prevention and control, providing the most effective means of curbing the spread of virus both inward and outward. This study attempts to explain the underlying factors and mechanisms that shape the community epidemic prevention and control capacity (CEPCC). Methods: We adopted a resilience-based perspective and drew on a sample of 20 exemplary anti-epidemic communities in China. By constructing an analytical framework and employing the fuzzy set qualitative comparative analysis method (fsQCA), we explored how four conditional variables-infrastructure completeness, community self-organizing ability, redundancy of community resources, and stability of regional economic development-and their various configurations impact the CEPCC. Results: Our findings reveal that the four conditional variables, when considered in isolation, cannot effectively enhance the CEPCC. Instead, four configuration pathways with mixed conditional variables exist. Notably, community self-organizing ability emerges as a vital condition for effectively strengthening the CEPCC. Discussion: This study identifies four pathways to improve the CEPCC and confirms the validity of the data results through case studies. Conclusions of this research contribute to a more nuanced understanding of the factors influencing the CEPCC, which can help communities to better plan and prepare for future epidemics and ensure better response and adaptation to the impacts of future emergencies.

Indexed as

COVID-19EpidemicsChinaDisease OutbreaksEconomic DevelopmentHumansChinacommunity epidemic prevention and control capacitycommunity resilienceCOVID-19 epidemicqualitative comparative analysis

Identifiers

PMID38249420
PMCPMC10796796

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Registered trials

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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.