Mediation analysis of conspiratorial thinking and anti-expert sentiments on vaccine willingness.
Bibliographic record
Abstract
OBJECTIVE: Vaccines are an effective means to reduce the spread of diseases, but they are sometimes met with hesitancy that needs to be understood. METHOD: In this study, we analyzed data from a large, cross-country survey conducted between June and August 2021 in 43 countries (N = 15,740) to investigate the roles of trust in government and science in shaping vaccine attitudes and willingness to be vaccinated. RESULTS: Despite significant variability between countries, we found that both forms of institutional trust were associated with a higher willingness to receive a COVID-19 vaccine. Furthermore, we found that conspiratorial thinking and anti-expert sentiments predicted reduced trust in government and science, respectively, and that trust mediated the relationship between these two constructs and ultimate vaccine attitudes. Although most countries displayed similar relationships between conspiratorial thinking and anti-expert sentiments, trust in government and science, and vaccine attitudes, we identified three countries (Brazil, Honduras, and Russia) that demonstrated significantly altered associations between the examined variables in terms of significant random slopes. CONCLUSIONS: Cross-country differences suggest that local governments' support for COVID-19 prevention policies can influence populations' vaccine attitudes. These findings provide insight for policymakers to develop interventions aiming to increase trust in the institutions involved in the vaccination process. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".