Show simple item record

dc.contributor.authorGravelle, TB
dc.contributor.authorPhillips, JB
dc.contributor.authorReifler, J
dc.contributor.authorScotto, TJ
dc.date.accessioned2023-04-27T13:03:54Z
dc.date.issued2022-03-29
dc.date.updated2023-04-27T11:52:10Z
dc.description.abstractVaccine hesitancy is a significant impediment to global efforts to vaccinate against the SARS-CoV-2 virus at levels that generate herd immunity. In this article, we show the utility of an inductive approach - latent class analysis (LCA) - that allows us to characterize the size and nature of different vaccine attitude groups; and to compare how these groups differ across countries as well as across demographic subgroups within countries. We perform this analysis using original survey data collected in the US, UK, and Canada. We also show that these classes are strongly associated with SARS-CoV-2 vaccination intent and perceptions of the efficacy and safety of the COVID-19 vaccines, suggesting that attitudes about vaccines to fight the novel coronavirus pandemic are well explained by latent vaccine attitudes that precede the pandemic. More specifically, we find four substantive classes of vaccine attitudes: strong supporters, supporters with concerns, vaccine hesitant, and "anti-vax" as well as a fifth measurement error class. The strong "anti-vax" sentiment class is small in all three countries, while the strong supporter class is the largest across all three countries. We observe different distributions of class assignments in different demographic groups - most notably education and political leaning (partisanship and ideology).en_GB
dc.description.sponsorshipEconomic and Social Research Council (ESRC)en_GB
dc.format.extent2008214-
dc.format.mediumPrint-Electronic
dc.identifier.citationVol. 18(1), article 2008214en_GB
dc.identifier.doihttps://doi.org/10.1080/21645515.2021.2008214
dc.identifier.grantnumberES/V004883/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/133041
dc.identifierORCID: 0000-0002-1116-7346 (Reifler, Jason)
dc.language.isoenen_GB
dc.publisherTaylor & Francisen_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/35349385en_GB
dc.rights© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_GB
dc.subjectCOVIDen_GB
dc.subjectanti-vaxen_GB
dc.subjectlatent class modelingen_GB
dc.subjectvaccine hesitancyen_GB
dc.subjectvaccinesen_GB
dc.titleEstimating the size of "anti-vax" and vaccine hesitant populations in the US, UK, and Canada: comparative latent class modeling of vaccine attitudesen_GB
dc.typeArticleen_GB
dc.date.available2023-04-27T13:03:54Z
dc.identifier.issn2164-5515
exeter.place-of-publicationUnited States
dc.descriptionThis is the final version. Available on open access from Taylor & Francis via the DOI in this recorden_GB
dc.identifier.eissn2164-554X
dc.identifier.journalHuman Vaccines and Immunotherapeuticsen_GB
dc.relation.ispartofHum Vaccin Immunother, 18(1)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2021-11-16
dc.rights.licenseCC BY
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-12-31
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-04-27T13:00:57Z
refterms.versionFCDVoR
refterms.dateFOA2023-04-27T13:03:58Z
refterms.panelCen_GB
refterms.dateFirstOnline2022-03-29


Files in this item

This item appears in the following Collection(s)

Show simple item record

© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's licence is described as © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.