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Predicting willingness to be vaccinated for Covid-19: Evidence from New Zealand
Governments around the world are seeking to slow the spread of Covid-19 and reduce hospitalisations by encouraging mass vaccinations for Covid-19. The success of this policy depends on most of the population accepting the vaccine and then being vaccinated. Understanding and predicting the motivation...
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Published in: | PloS one 2022-04, Vol.17 (4), p.e0266485 |
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description | Governments around the world are seeking to slow the spread of Covid-19 and reduce hospitalisations by encouraging mass vaccinations for Covid-19. The success of this policy depends on most of the population accepting the vaccine and then being vaccinated. Understanding and predicting the motivation of individuals to be vaccinated is, therefore, critical in assessing the likely effectiveness of a mass vaccination programme in slowing the spread of the virus. In this paper we draw on the I3 Response Framework to understand and predict the willingness of New Zealanders to be vaccinated for Covid-19. The Framework differs from most studies predicting willingness to be vaccinated because it is based on the idea that the willingness to adopt a behaviour depends on both involvement (a measure of motivational strength) with the behaviour and attitudes towards the behaviour. We show that predictions of individuals' willingness to be vaccinated are improved using involvement and attitudes together, compared to attitudes alone. This result has important implications for the implementation of mass vaccination programmes for Covid-19. |
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This result has important implications for the implementation of mass vaccination programmes for Covid-19.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0266485</identifier><identifier>PMID: 35390053</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Attitudes ; Biology and Life Sciences ; Computer and Information Sciences ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; COVID-19 vaccines ; COVID-19 Vaccines - therapeutic use ; Decision making ; Health behavior ; Humans ; Immunization ; Mass Vaccination ; Medicine and Health Sciences ; Motivation ; New Zealand - epidemiology ; People and places ; SARS-CoV-2 ; Social Sciences ; Vaccination ; Vaccines ; Viruses</subject><ispartof>PloS one, 2022-04, Vol.17 (4), p.e0266485</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Kaine et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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subjects | Analysis Attitudes Biology and Life Sciences Computer and Information Sciences COVID-19 COVID-19 - epidemiology COVID-19 - prevention & control COVID-19 vaccines COVID-19 Vaccines - therapeutic use Decision making Health behavior Humans Immunization Mass Vaccination Medicine and Health Sciences Motivation New Zealand - epidemiology People and places SARS-CoV-2 Social Sciences Vaccination Vaccines Viruses |
title | Predicting willingness to be vaccinated for Covid-19: Evidence from New Zealand |
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