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Blaming the unvaccinated during the COVID-19 pandemic: the roles of political ideology and risk perceptions in the USA

Abstract

Individuals unvaccinated against COVID-19 (C19) experienced prejudice and blame for the pandemic. Because people vastly overestimate C19 risks, we examined whether these negative judgements could be partially understood as a form of scapegoating (ie, blaming a group unfairly for an undesirable outcome) and whether political ideology (previously shown to shape risk perceptions in the USA) moderates scapegoating of the unvaccinated. We grounded our analyses in scapegoating literature and risk perception during C19. We obtained support for our speculations through two vignette-based studies conducted in the USA in early 2022. We varied the risk profiles (age, prior infection, comorbidities) and vaccination statuses of vignette characters (eg, vaccinated, vaccinated without recent boosters, unvaccinated, unvaccinated-recovered), while keeping all other information constant. We observed that people hold the unvaccinated (vs vaccinated) more responsible for negative pandemic outcomes and that political ideology moderated these effects: liberals (vs conservatives) were more likely to scapegoat the unvaccinated (vs vaccinated), even when presented with information challenging the culpability of the unvaccinated known at the time of data collection (eg, natural immunity, availability of vaccines, time since last vaccination). These findings support a scapegoating explanation for a specific group-based prejudice that emerged during the C19 pandemic. We encourage medical ethicists to examine the negative consequences of significant C19 risk overestimation among the public. The public needs accurate information about health issues. That may involve combating misinformation that overestimates and underestimates disease risk with similar vigilance to error.

  • COVID-19
  • political ideology
  • scapegoating
  • vaccination
  • unvaccinated

Data availability statement

Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information.

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