Evidence map›Paper›PMID 33540869›Full record

ArticleInternational journal of environmental research and public health2021

Determinants of the Perceived Credibility of Rebuttals Concerning Health Misinformation.

Yujia Sui, Bin Zhang

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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

2 authors.

Yujia SuiSchool of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Bin ZhangSchool of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Users provide and share information with a broad audience on different forms of social media; however, information accuracy is questionable. Currently, the health information field is severely affected by misinformation. Thus, addressing health misinformation is integral for enhancing public health. This research can help relevant practitioners (i.e., government officials, medical and health service personnel, and educators) find the most effective correctional interventions for governing health misinformation. We constructed a theoretical model for credibility-oriented determinants refuting misinformation based on the elaboration likelihood model. We aggregated 415 pieces of valid data through a questionnaire survey. A partial least squares structural equation model evaluated this research model. The results indicated that both perceived information quality and perceived source credibility can enhance perceived information credibility. Under some circumstances, the influence of information quality on information credibility may be more important than that of the information source. However, the cognitive conflict and knowledge self-confidence of information receivers weaken the influence of information quality on information credibility. In contrast, cognitive conflict can strengthen the influence of source credibility on information credibility. Further, perceived information quality can be affected by information usefulness, understandability, and relevance, while perceived source reliability can be affected by source expertise and authority.

Indexed as

CommunicationSocial MediaHumansOrganizationsReproducibility of ResultsSurveys and Questionnaireshealth misinformationrebuttalsocial mediastructural equation model

Identifiers

PMID33540869
PMCPMC7908451

What OpenQuestion holds

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LicenceCC BY
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.