COVID-19 vaccine skeptics are persuaded by pro-vaccine expert consensus messaging.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To further understand how to combat COVID-19 vaccination hesitancy, we examined the effects of pro-vaccine expert consensus messaging on lay attitudes about vaccine safety and intention to get a COVID-19 vaccine. We surveyed 729 unvaccinated individuals from four countries in the early stages of the pandemic and 472 unvaccinated individuals from two countries after 2 years of the pandemic. We found belief of vaccine safety strongly correlated with intention to vaccinate in the first sample and less strongly in the second. We also found that consensus messaging improved attitudes toward vaccination even for participants who did not believe the vaccine is safe nor intended to get it. The persuasiveness of expert consensus was unaffected by exposing participants' lack of knowledge about vaccines. We conclude that highlighting expert consensus may be a way to increase support toward COVID-19 vaccination in those hesitant or skeptical. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it