Evidence map›Paper›PMID 39381809›Full record

ArticleDigital health

Bottom-up actions: Examining perceived influence of pandemic news, information relevance, perceived media credibility, and pro-community participation.

Caixie Tu, Xigen Li

Abstract read
In one paragraph

Article in Digital health. 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

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

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

2 authors.

Caixie TuSchool of Journalism and Communication, Shanghai University, Shanghai, China.ORCID https://orcid.org/0000-0003-2210-2094
Xigen LiSchool of Journalism and Communication, Shanghai University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: When hyper-infectious diseases sweep over the world, pro-community participation has been found to effectively curb the spread of viruses. This study explores the associations among media-related perceptions and media users' pro-community participation during the peak of the 2022 COVID-19 outbreaks in China. Methods: A cross-sectional survey was conducted among 976 Chinese media users in April 2022 to collect data on their pro-community participation and perceptions of pandemic news influence, information relevance, and credibility of traditional media and social media. Hierarchical regression analyses were run to analyze the associations between these perceptual variables and pro-community participation. Results: The findings revealed that information relevance was positively associated with perceived news influence on oneself and pro-community participation. Perceived credibility of traditional media was positively associated with perceived news influence on both oneself and others. Perceived credibility of social media was positively associated with perceived news influence on others. Additionally, perceived credibility of traditional media positively moderated the association between information relevance and perceived news influence on others. Conclusions: Information relevance and perceived media credibility play significant roles in shaping media user' perceptions of news influence and their subsequent pro-community behaviors. Higher perceived media credibility can produce a broader impact on perceived news influence on both media users themselves and others, highlighting its importance in public health communication strategies. These insights can inform media practices and public health policies to enhance community participation during public health crises.

Indexed as

media credibilityPro-community participationpublic health crisesthird-person effect

Identifiers

PMID39381809
PMCPMC11459497

What OpenQuestion holds

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LicenceCC BY-NC
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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.