Evidence map›Paper›PMID 36570632›Full record

ArticleInternational journal of disaster risk reduction : IJDRR2021

The more actions, the higher the performance evaluation? Evidence from the crisis management of COVID-19 in China.

Qi Bian, Xiaojun Zhang, Qingduo Mao

Open access · greenAbstract read
In one paragraph

Article in International journal of disaster risk reduction : IJDRR, 2021. 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
8.3field-weighted citation impact, top 3% of its field
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, 26 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Qi BianSchool of Political Science and Public Administration, Shandong University, Qingdao, 266237, China.
Xiaojun ZhangSchool of Economics and Management, Fuzhou University, Fuzhou, 350108, China.
Qingduo MaoSchool of International Affairs and Public Administration, Ocean University of China, Qingdao, 266100, China.
Fuzhou University · CNOcean University of China · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Governments around the world have taken measures in fighting against COVID-19, but how the government's response affects the public evaluation of government performance in crisis remains to be examined. The study investigated how government actions, the public's trust in government, risk perception, and negative emotions influenced the public evaluation of government performance based on a primary survey. The findings showed that: (1) government actions and public evaluation of government performance in crisis are significantly and positively associated; (2) trust in government can positively lead to the public evaluation of government performance in crisis; (3) risk perception and negative emotions can negatively moderate and attenuate the previous positive relationship. The research revealed that the public evaluation of government performance is formed by the government actions through mediation effect of trust in government and also moderation effect of risk perception and emotional factors. Thus, this study innovatively developed a moderated mediation model of public perceived government performance which may serve as a basis for improving public evaluation while dealing with the crisis in other countries and regions.

Indexed as

Government actionsModerated mediation modelNegative emotionsPublic evaluation of government performanceRisk perceptionTrust in government

Identifiers

PMID36570632
PMCPMC9764212
OpenAlexW3159696946

What OpenQuestion holds

Textmetadata
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.