Evidence map›Paper›PMID 36497869›Full record

ArticleInternational journal of environmental research and public health2022

Factors Contributing to Citizens' Participation in COVID-19 Prevention and Control in China: An Integrated Model Based on Theory of Planned Behavior, Norm Activation Model, and Political Opportunity Structure Theory.

Xiaojie Zhang, Lili Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

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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.

Xiaojie ZhangDepartment of Public Administration, School of Humanities & Law, Northeastern University, Shenyang 110169, China.ORCID 0000-0003-1999-9363
Lili WangParty School of Weihai Municipal Committee of Communist Party of China, Weihai 264213, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chinese citizens' participation in COVID-19 prevention and control has made great contributions to the successful fight against the pandemic. The factors that have impacted citizens' participation have rarely been reported based on both social-psychological and political environmental theories. This paper presented a study to explore the determinants of Chinese citizens' participation in COVID-19 prevention and control based on a combined model of the theory of planned behavior, the norm activation model, and political opportunity structure theory. A dataset involving 463 respondents from Harbin in northeast China was acquired and analyzed. The results showed that the comprehensive model explained 62.9% of the total variance in citizens' participation behavior. The openness to public participation not only significantly directly influenced citizens' participation but also indirectly affected participation behaviors through attitude and perceived behavioral control, both of which were important mediators and had the greatest overall impacts. The awareness of consequences and subjective norms were crucial antecedents to the activation of other influencing factors. Personal norms indirectly affected participation behavior through the mediation of attitude. The empirical results showed the comprehensiveness, effectiveness, and high explanatory power of the postulated model. The study also provides both new theoretical perspectives for explaining public participation and useful practical implications for future policy development in promoting citizens' participation in public health emergency management.

Indexed as

COVID-19AttitudeCommunity ParticipationHumansPandemicsTheory of Planned Behaviorcitizen participationCOVID-19determinantsnorm activation modelopenness to public participationpolitical opportunity structuretheory of planned behavior

Identifiers

PMID36497869
PMCPMC9739160

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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.