Vaccination hesitancy and conspiracy beliefs in the UK during the Covid-19 pandemic
Bibliographic record
Abstract
Abstract Objective: Vaccination hesitancy and conspiracy beliefs are a threat to achieving population immunity in Covid-19. This study aimed to clarify the association between these and incentives to vaccination in the UK. Design: In a longitudinal study, we collected UK public data at three time points: 1) before and 2) after the development of a vaccine, and 3) after the vaccination programme was underway. Main Outcome Measures: Vaccination hesitancy; general and Covid-19 specific concerns about vaccination; belief in conspiracy theories. Results: Vaccination hesitancy decreased between Times 1 (54%) and 3 (13%). Most concerns and reported incentives related to safety, though at Time 2, incentives included endorsement by trusted public figures. We found only small effects of conspiracy belief, and only at Time 1. A minority of participants remained anti-vaccination and stated nothing would change their minds. Conclusion: Vaccination hesitancy seems to be falling the UK. However, anxiety about safety remains and could jeopardise the vaccination programme should any adverse effects be reported. Conspiracy beliefs seem to play only a minor role in hesitancy and may continue to decrease in importance with a successful vaccination programme. Understanding motivations behind vaccination hesitancy is vital if we are to achieve population immunity.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".