Evidence map›Paper›PMID 41602077›Full record

ArticleFrontiers in public health2025

Has the COVID-19 pandemic enhanced the capacity of rural public health emergency management? Evidence from Jiangsu Province of China.

Ming Cao, Yajing Zhang

Abstract read
In one paragraph

Article in Frontiers in public health, 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

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

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

2 authors.

Ming CaoSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou, China.
Yajing ZhangSchool of Public Policy & Management, China University of Mining and Technology, Xuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health emergencies pose both challenges and opportunities for improving rural public health emergency management capacity. This study develops a comprehensive evaluation framework comprising 22 indicators across four dimensions: emergency infrastructure, emergency preparedness, emergency response, and emergency recovery. Using expert scoring, the analytic hierarchy process (AHP), and the entropy weight method, the rural public health emergency management capacity of Jiangsu Province was quantitatively assessed for the period 2016-2023 to compare changes before and after the COVID-19 pandemic. The results show that the outbreak of COVID-19 significantly enhanced the overall capacity of rural public health emergency management in Jiangsu Province, with the composite score increasing from 4.97 to 6.84. Among the four dimensions, emergency infrastructure capacity experienced the most pronounced improvement, rising from 4.02 to 9.66, whereas gains in emergency preparedness, response, and recovery capacities were relatively limited. These findings indicate that major public health emergencies can accelerate infrastructure development but do not automatically strengthen institutional preparedness or recovery mechanisms in rural areas. Accordingly, this study suggests increasing financial investment, promoting multi-actor collaborative governance, and improving public health emergency management mechanisms to further enhance rural public health emergency capacity in China.

Indexed as

Civil DefenseCOVID-19Disaster PlanningPandemicsPublic HealthRural Health ServicesChinaHumansPublic Health InfrastructureSARS-CoV-2COVID-19emergency management capabilitiespublic health emergenciesrural public healthrural public health emergency management

Identifiers

PMID41602077
PMCPMC12833016

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.