Blaming the unvaccinated during the COVID-19 pandemic: the roles of political ideology and risk perceptions in the USA
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".