Evidence map›Paper›PMID 34948888›Full record

ArticleInternational journal of environmental research and public health2021

Influencing Factors of Public Satisfaction with COVID-19 Prevention Services Based on Structural Equation Modeling (SEM): A Study of Nanjing, China.

Wei Chen, Yijun Shi, Liwen Fan, Lijun Huang, Jingyi Gao

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. 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

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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

5 authors.

Wei ChenSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Yijun ShiSchool of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China.ORCID 0000-0003-3099-4379
Liwen FanSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Lijun HuangJiangsu Provincial Planning and Design Group, Nanjing 210023, China.
Jingyi GaoGraduate School of Engineering, Tohoku University, Sendai 980-0845, Japan.ORCID 0000-0003-1887-3240

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Service satisfaction with public policies is an important component of public service quality management, which is of great significance to the improvement of public service quality. Based on an online questionnaire survey and in combination with the characteristics of public policies and services, in this study the influencing factors of residents' satisfaction with COVID-19 pandemic prevention services were analyzed with structural equation modeling. The results reveal that the data fit the model well, and all the hypotheses formulated in this study were supported. Among the factors that were found to directly affect residents' satisfaction with pandemic prevention services, perceived quality (PQ) has the greatest impact on satisfaction, followed by the disaster situation (DS) and policy expectation (PE). The observed variables that have significant impacts on the latent variables were also explored. Regarding the main findings, the residents who were seriously affected by the pandemic tended to have lower satisfaction with the policies and services provided by the government. Moreover, the improvement of PQ was found to significantly increase pandemic prevention service satisfaction (SS). Finally, the residents with a good psychological status during the pandemic were found to have higher satisfaction. According to the results, implications for the prevention and control practices of similar public health emergencies are proposed.

Indexed as

COVID-19Personal SatisfactionChinaHumansLatent Class AnalysisPandemicsPatient SatisfactionSARS-CoV-2COVID-19emergency managementpublic healthpublic satisfactionSEM

Identifiers

PMID34948888
PMCPMC8704536

What OpenQuestion holds

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LicenceCC BY
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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.