Conspiratorial beliefs and reduced vaccine acceptance: Understanding the role of perspective-taking.
Bibliographic record
Abstract
Surges in infectious diseases often bring illness and conspiratorial beliefs. Such beliefs can hinder the adoption of public health advice, including vaccination. Because conspiratorial beliefs are difficult to reduce once entrenched, it is essential to explore strategies that mitigate their impact on vaccine acceptance. We present perspective-taking as a novel intervention, testing whether the negative association between conspiratorial beliefs and vaccine acceptance is weaker when participants take the perspective of someone holding positive vaccine attitudes. In Studies 1A-1C, participants read excerpts from interviews with COVID-19-vaccinated individuals. Study 2 examined live conversations with individuals holding positive vaccine attitudes and tested the durability of the effects by measuring vaccine acceptance 2 weeks later, assessing whether the moderating effect of perspective-taking arose from enhanced psychological closeness. Studies 3A-3B extended the hypotheses to a fictitious disease to examine generalizability beyond COVID-19. Study 3A used a similar paradigm to Studies 1A-1C and tested the same hypotheses as Study 2. Study 3B assessed the moderating effect of perspective-taking through a public service announcement-style video designed to enhance ecological validity. We found general support for our hypotheses. This research is significant because it can lead to the development of strategies to combat vaccine hesitancy. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.002 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".