Prebunking messaging to inoculate against COVID-19 vaccine misinformation: an effective strategy for public health
Bibliographic record
Abstract
Background Vaccination coverage needs to reach more than 80% to resolve the COVID-19 pandemic, but vaccine hesitancy, fuelled by misinformation, may jeopardize this goal. Unvaccinated older adults are not only at risk of COVID-19 complications but may also be misled by false information. Prebunking, based on inoculation theory, involves ‘forewarning people [of] and refuting information that challenges their existing belief or behavior’.Objective To assess the effectiveness of inoculation communication strategies in countering disinformation about COVID-19 vaccines among Canadians aged 50 years and older, as measured by their COVID-19 vaccine intentions.Method Applying an online experiment with a mixed pre–post design and a sample size of 2500 participants, we conducted a national randomized survey among English and French-speaking Canadians aged 50 years and older in March 2021. Responses to two different disinformation messages were evaluated. Our primary outcome was the intention to receive a COVID-19 vaccine, with attitudes toward COVID-19 vaccine a secondary outcome. The McNemar test and multivariate logistic regression analysis on paired data were conducted when the outcome was dichotomized. Wilcoxon sign rank test and Kruskal–Wallis were used to test difference scores between pre- and post-tests by condition.Results Group comparisons between those who received only disinformation and those who received the inoculation message show that prebunking messages may safeguard intention to get vaccinated and have a protective effect against disinformation.Conclusion Prebunking messages should be considered as one strategy for public health communication to combat misinformation.
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 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".