Evidence map›Paper›PMID 39559375›Full record

ArticleFrontiers in public health2024

Research on the evaluation of emergency management capability for urban public health emergencies under the perspective of resilience-a case study of Henan Province, China.

Yu Hao, Yuxin Tie, Lijun Zhang, Fan Zhang, Chaolun Sun

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

5 authors.

Yu HaoSafety and Emergency Management Research Center, Henan Polytechnic University, Jiaozuo, China.
Yuxin TieSchool of Information Management, Central China Normal University, Wuhan, China.
Lijun ZhangSafety and Emergency Management Research Center, Henan Polytechnic University, Jiaozuo, China.
Fan ZhangSafety and Emergency Management Research Center, Henan Polytechnic University, Jiaozuo, China.
Chaolun SunSafety and Emergency Management Research Center, Henan Polytechnic University, Jiaozuo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of the economy and society, the likelihood of sudden public health emergencies in urban areas continues to rise. In particular, major infectious diseases that have gained global attention, such as the SARS virus, H1N1 influenza, Ebola outbreak, and COVID-19 pandemic, have presented significant challenges to urban emergency management systems. Evaluating emergency management capability is a fundamental requirement for developing emergency response capacity. To this end, this study combines the theory of resilience with the theory of full-process equilibrium emergency management, selects 31 evaluation indicators from six key aspects: preparedness, forewarning, mitigation, disposal, recovery, and learning. The indicator weights are determined using the AHP-Entropy Weight Method, and a TOPSIS model is constructed to assess the emergency management capability of urban public health emergencies. The model's applicability is validated by examining 18 cities in Henan Province. The findings suggest that Jiaozuo, Hebi, Zhengzhou, and Luohe possess relatively robust emergency management capabilities for sudden public health incidents, whereas Kaifeng, Pingdingshan, and Shangqiu exhibit weaker capabilities.

Indexed as

COVID-19EmergenciesPublic HealthChinaCitiesDisaster PlanningDisease OutbreaksHumansSARS-CoV-2capacity evaluationemergency management capacitypublic health emergenciesthe full process balance of emergency managementTOPSIS model

Identifiers

PMID39559375
PMCPMC11571321

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LicenceCC BY
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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.