Evidence map›Paper›PMID 37325904›Full record

ArticleRevista espanola de salud publica2023

[Evaluation of the Dunning Kruger effect in relation to vaccination: a study of messages on the social network LinkedIn.]

Marta Sánchez Ordóñez, Pedro Emilio Bermejo Velasco, Ángel Luis Rubio Moraga

Abstract readEnglish Abstract
In one paragraph

Article in Revista espanola de salud publica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Marta Sánchez OrdóñezDepartamento de Periodismo y Comunicación Global, Facultad de Ciencias de la Información, Universidad Complutense de Madrid. Madrid. España.
Pedro Emilio Bermejo VelascoDepartamento de Neurología, Hospital universitario Puerta de Hierro. Madrid. España.
Ángel Luis Rubio MoragaDepartamento de Periodismo y Comunicación Global, Facultad de Ciencias de la Información, Universidad Complutense de Madrid. Madrid. España.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe Dunning-Kruger effect refers to an excess of confidence regarding one's abilities and knowledge; trust leads to transmitting information in an assertive manner, regardless of its validity or veracity, of experts, but of great impact on public opinion. This study evaluated the existence of the Dunning-Kruger effect in messages related to vaccination against COVID-19 on LinkedIn.

methods448 messages were evaluated and the authors' knowledge and training on the subject were related. In the statistical treatment, the Chi-square test was performed to determine if there is a significant association between the variables, establishing the level of significance at P<0.05. These procedures were carried out using SPSS statistical software.

results448 messages were analyzed. Of these, 153 reflected very high certainty, 115 medium certainty, 107 low certainty and 73 reflected doubts. The group that issued the most messages with absolute certainty (41.8%) was the group with minimal knowledge about COVID-19. Of this group without knowledge on the subject, only 7.1% expressed messages without expressing certainty. The group with very high knowledge on the subject was more likely to reflect uncertainty, communicating 15.7% of the messages with absolute certainty and 37.1% with zero certainty.

conclusionsIt is obtained that those people with less knowledge express their messages more assertively and present less acceptance of the COVID-19 vaccine in their speeches. The presence of the Dunning-Kruger effect in relation to COVID-19 vaccination is demonstrated.

Indexed as

COVID-19COVID-19 VaccinesHumansSocial NetworkingSpainVaccinationCOVID-19 VaccinesAnti-vaccinesCOVID-19Dunning-KrugerHeuristic biasesSocial networksSocial psychologySpainVaccinationVaccination strategies

Identifiers

PMID37325904
PMCPMC10541263

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LicenceCC BY-NC-ND
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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.