Addressing Myths and Vaccine Hesitancy: A Randomized Trial
Bibliographic record
Abstract
OBJECTIVES Evidence on repeating vaccination misinformation or "myths" in debunking text is inconclusive; repeating myths may unintentionally increase agreement with myths or help discredit myths. In this study we aimed to compare the effect of repeating vaccination myths and other text-based debunking strategies on parents’ agreement with myths and their intention to vaccinate their children. METHODS For this online experiment we recruited 788 parents of children aged 0 to 5 years; 454 (58%) completed the study. We compared 3 text-based debunking strategies (repeating myths, posing questions, or making factual statements) and a control. We measured changes in agreement with myths and intention to vaccinate immediately after the intervention and at least 1 week later. The primary analysis compared the change in agreement with vaccination myths from baseline, between groups, at each time point after the intervention. RESULTS There was no evidence that repeating myths increased agreement with myths compared with the other debunking strategies or the control. Posing questions significantly decreased agreement with myths immediately after the intervention compared with the control (difference: −0.30 points, 99.17% confidence interval: −0.58 to −0.02, P = .004, d = 0.39). There was no evidence of a difference between other debunking strategies or the control at either time point, or on intention to vaccinate. CONCLUSIONS Debunking strategies that repeat vaccination myths do not appear to be inferior to strategies that do not repeat myths.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".