ǂa ǂlatent profile analysis of COVID-19 vaccination rejection
Monika Lamot (Author), Katja Kerman (Author), Andrej Kirbiš (Author)

Abstract

Trust in science and scientists, satisfaction with the national government, and endorsement of conspiracy theories are important factors in the decision to be vaccinated. In this study, we investigated whether there are different profiles of individuals depending on the above factors and whether they differ in their sociodemographic composition. We used data from Round 10 of the European Social Survey for Slovenia, employing a nationally representative sample of 1252 participants. Based on latent profile analysis, three distinct profiles emerged: Profile 1 expressed moderate trust in science, satisfaction with government, and high endorsement of conspiracies; Profile 2 expressed low trust and satisfaction and moderate endorsement of conspiracies; Profile 3 expressed high trust and satisfaction and low beliefs in conspiracy theories. In addition, Profile 3 expressed the strongest support for vaccination and Profile 2 the lowest. Our results suggest that distrust, dissatisfaction, and the presence of conspiracy theories are the “perfect storm” for vaccination rejection. In contrast, despite conspiracy theories, a certain level of trust and satisfaction may reduce vaccination rejection.

Keywords

zaupanje;znanost;vlada;teorije zarote;cepljenje proti covidu-19;analiza latentnih profilov;trust;science;government;COVID-19 vaccination;latent profile analysis;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UM FF - Faculty of Arts
Publisher: MDPI
UDC: [316.653:351.774.7]:159.9
COBISS: 118416643 Link will open in a new window
ISSN: 1660-4601
Views: 8
Downloads: 0
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: zaupanje;znanost;vlada;teorije zarote;cepljenje proti covidu-19;analiza latentnih profilov;
Type (COBISS): Article
Pages: str. 1-11
Volume: ǂVol. ǂ19
Issue: ǂiss. ǂ16
Chronology: 2022
DOI: 10.3390/ijerph191610096
ID: 23412319
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