Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy

Detalhes bibliográficos
Autor(a) principal: Fernandes, Nuno
Data de Publicação: 2021
Outros Autores: Costa, Daniela, Costa, Diogo, Keating, José, Arantes, Joana
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/74730
Resumo: Data is available at https://doi.org/10.17605/OSF.IO/TR2P3. Analysis code was written in R and Python and is available at https://nunokf.github.io/Predicting-COVID-19-Vaccination-Intention-The-Determinants-of-Vaccine-Hesitancy-/.
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spelling Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancyVaccine hesitancyCOVID-19Vaccination barriersChildren vaccinationMachine learningCiências Médicas::Ciências da SaúdeCiências Sociais::PsicologiaScience & TechnologySaúde de qualidadeData is available at https://doi.org/10.17605/OSF.IO/TR2P3. Analysis code was written in R and Python and is available at https://nunokf.github.io/Predicting-COVID-19-Vaccination-Intention-The-Determinants-of-Vaccine-Hesitancy-/.Do people want to be vaccinated against COVID-19? Herd immunity is dependent on individuals’ willingness to be vaccinated since vaccination is not mandatory. Our main goal was to investigate people’s intention to be vaccinated and their intentions to vaccinate their children. Moreover, we were interested in understanding the role of the personal characteristics, psychological factors, and the lockdown context on that decision. Therefore, we conducted an online survey during the lockdown in Portugal (15 January 2021 until 14 March 2021). Participants completed a socio-demographic questionnaire, questions about their intentions of being vaccinated, concerns about the vaccine, a COVID-19 attitudes and beliefs scale, a COVID-19 vaccine attitudes and beliefs scale, and the Domain-Specific Risk-Taking (DOSPERT) Scale. Our results showed that from the 649 participants, 63% of the participants reported being very likely to have the vaccine, while 60% reported being very likely to vaccinate their children. We conducted two linear regression models, explaining 65% of the variance for personal vaccination and 56% of the variance for children vaccination. We found that the COVID-19 vaccine general beliefs and attitudes were the main determinants of vaccination intention. Additionally, our proposed artificial neural network model was able to predict with 85% accuracy vaccination intention. Thus, our results suggest that psychological factors are an essential determinant of vaccination intention. Thus, public policy decision makers may use these insights for predicting vaccine hesitancy and designing effective vaccination communication strategies.This research was funded by Fundação para a Ciência e Tecnologia (FCT), grant number UIDB/PSI/01662/2020.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoFernandes, NunoCosta, DanielaCosta, DiogoKeating, JoséArantes, Joana2021-10-112021-10-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/74730engFernandes, N.; Costa, D.; Costa, D.; Keating, J.; Arantes, J. Predicting COVID-19 Vaccination Intention: The Determinants of Vaccine Hesitancy. Vaccines 2021, 9, 1161. https://doi.org/10.3390/vaccines91011612076-393X10.3390/vaccines9101161https://www.mdpi.com/2076-393X/9/10/1161info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:35:08Zoai:repositorium.sdum.uminho.pt:1822/74730Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:30:56.966530Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
title Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
spellingShingle Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
Fernandes, Nuno
Vaccine hesitancy
COVID-19
Vaccination barriers
Children vaccination
Machine learning
Ciências Médicas::Ciências da Saúde
Ciências Sociais::Psicologia
Science & Technology
Saúde de qualidade
title_short Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
title_full Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
title_fullStr Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
title_full_unstemmed Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
title_sort Predicting COVID-19 vaccination intention: the determinants of vaccine hesitancy
author Fernandes, Nuno
author_facet Fernandes, Nuno
Costa, Daniela
Costa, Diogo
Keating, José
Arantes, Joana
author_role author
author2 Costa, Daniela
Costa, Diogo
Keating, José
Arantes, Joana
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Fernandes, Nuno
Costa, Daniela
Costa, Diogo
Keating, José
Arantes, Joana
dc.subject.por.fl_str_mv Vaccine hesitancy
COVID-19
Vaccination barriers
Children vaccination
Machine learning
Ciências Médicas::Ciências da Saúde
Ciências Sociais::Psicologia
Science & Technology
Saúde de qualidade
topic Vaccine hesitancy
COVID-19
Vaccination barriers
Children vaccination
Machine learning
Ciências Médicas::Ciências da Saúde
Ciências Sociais::Psicologia
Science & Technology
Saúde de qualidade
description Data is available at https://doi.org/10.17605/OSF.IO/TR2P3. Analysis code was written in R and Python and is available at https://nunokf.github.io/Predicting-COVID-19-Vaccination-Intention-The-Determinants-of-Vaccine-Hesitancy-/.
publishDate 2021
dc.date.none.fl_str_mv 2021-10-11
2021-10-11T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/74730
url http://hdl.handle.net/1822/74730
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Fernandes, N.; Costa, D.; Costa, D.; Keating, J.; Arantes, J. Predicting COVID-19 Vaccination Intention: The Determinants of Vaccine Hesitancy. Vaccines 2021, 9, 1161. https://doi.org/10.3390/vaccines9101161
2076-393X
10.3390/vaccines9101161
https://www.mdpi.com/2076-393X/9/10/1161
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799132815664611328

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