Evidence map›Paper›PMID 34525258›Full record

ArticleThe International journal of health planning and management2022

Can closed-off management in communities alleviate the psychological anxiety and stress caused by the COVID-19 pandemic?

Yiwei Liu, Deshui Zhou, Ximei Geng

Abstract read
In one paragraph

Article in The International journal of health planning and management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

3 authors.

Yiwei LiuSchool of Government, Central University of Finance and Economics, Beijing, China.ORCID https://orcid.org/0000-0002-7747-4373
Deshui ZhouSchool of Finance and Public Management, Anhui University of Finance & Economics, Bengbu, China.ORCID https://orcid.org/0000-0001-6710-9439
Ximei GengSchool of Government, Central University of Finance and Economics, Beijing, China.

Funding

MOE (Ministry of Education in China) Project of Humanities and Social Sciences
6 · The paper itself

Abstract

Since the outbreak of the novel coronavirus (COVID-19) pandemic, the number of individuals infected with the COVID-19 has been increasing worldwide, posed a substantial threat to mental health. Therefore, discovering the means for blocking the transmission path of COVID-19 and improving mental health is of great significance. Based on a survey on the life of urban and rural residents in China (in 2020), the study applied the least squares method to analyse the effect of closed-off management on the psychological anxiety and psychological stress caused by the COVID-19 pandemic. Furthermore, the bootstrap decomposition method was used to test for the mediating effects of satisfaction with and confidence in pandemic prevention. The study found that closed-off management in communities exerted a negative effect on psychological anxiety and stress caused by the COVID-19 pandemic. Additionally, satisfaction with and confidence in pandemic prevention played a mediating role on the psychological anxiety and stress caused by the COVID-19 pandemic. Therefore, closed-off management in communities will not only prevent the spread of the COVID-19, but also improve satisfaction with and confidence in pandemic prevention and control. Furthermore, it will alleviate the psychological anxiety and stress caused by the COVID-19 pandemic.

Indexed as

COVID-19PandemicsAnxietyDepressionHumansSARS-CoV-2closed-off management in communitiesconfidence in pandemic preventionCOVID-19psychological anxietypsychological stresssatisfaction with pandemic prevention

Identifiers

PMID34525258
PMCPMC8652683

What OpenQuestion holds

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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.