Public Misperceptions of COVID-19 Vaccine Effectiveness and Waning: Experimental Evidence from Ireland
Bibliographic record
Abstract
Objectives The study set out to measure public understanding of COVID-19 vaccine effectiveness (VE) and how effectiveness wanes with time since vaccination. Because perceived VE is a strong predictor of vaccine uptake, measuring perceptions can inform public health policy and communications. Study DesignOnline randomised experiment.MethodThe study was undertaken in Ireland, which has high vaccination rates. A nationally representative sample (n=2,000) responded to a scenario designed to measure perceptions of COVID-19 VE against mortality. The length of time since vaccination in the scenario was randomly varied across four treatment arms (2 weeks, 3 months, 6 months, 9 months).ResultsThe public underestimates VE, with substantial variation in perceptions. A majority (57%) gave responses implying perceived VE against mortality of 0-85%, i.e. below scientific estimates. Among this group, mean perceived VE was just 49%. Over a quarter (26%) gave responses implying perceived VE greater than 95%, i.e. above scientific estimates. Comparing the four treatment groups, responses took no account of vaccine waning. Perceived VE was actually higher 9 months after vaccination than 2 weeks after vaccination. ConclusionDespite high vaccination rates, most of the public in Ireland underestimates VE. Furthermore, the general public has not absorbed the concept of vaccine waning in the months following vaccination. Both misperceptions may reduce vaccine uptake, unless public health authorities act to correct them through improved communication.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".