What nudges you to take a vaccine? Understanding behavioural drivers of COVID-19 vaccinations using large-scale experiments in the G-7 countries
Bibliographic record
Abstract
Introduction We present a unique multi-country, two-wave dataset of 42,417 survey responses drawn from nationally representative samples of citizens from the G-7 countries: Canada, France, Germany, Italy, Japan, UK, and USA. This data note outlines the motivation and methodology of the survey instrument and describes the measures contained in the dataset. We highlight areas for future research.Methods We fielded an online survey over two waves (January 27 to February 26 [n = 24,303] and wave 2 from March 6 to May 12 [n = 18,114]) measuring a range of demographic, social, political, and psychological variables. Samples were nationally representative by age, education, gender, and subnational region. Each wave included of three experiments (one conjoint and two between-subjects) to facilitate randomised evaluation of behavioural health policies promoting the uptake of COVID-19 booster vaccinations.Results The dataset has produced two peer-reviewed publications at the time of writing ([Banerjee, S., John, P., Nyhan, B., Hunter, A., Koenig, R., Lee-Whiting, B., Loewen, P. J., McAndrews, J., & Savani, M. M. (2024). Thinking about default enrollment lowers vaccination intentions and public support in G7 countries. PNAS Nexus, 3(4), pgae093]; [Koenig, R., Savani, M. M., Lee-Whiting, B., McAndrews, J., Banerjee, S., Hunter, A., John, P., Loewen, P. J., & Nyhan, B. (2024). Public support for more stringent vaccine policies increases with vaccine effectiveness. Scientific Reports, 14(1), 1748]). A summary report is posted online (https://www.thebritishacademy.ac.uk/publications/overcoming-barriers-to-vaccination-by-empowering-citizens-to-make-deliberate-choices/). Additional research outputs are currently under preparation.Discussion Our dataset combines observational and experimental data on behavioural health policies, offering numerous insights. First, the dataset's extensive size and geographical diversity enables comparative analysis of public health issues involving social, political, and behavioural factors. Second, the dataset is suited to advanced statistical methods that can explore heterogeneity in the uptake of behavioural health policies, such as vaccine nudges. Third, the timing of the data collection, coinciding with the rise of the Omicron variant, provides valuable insights into why some previously vaccinated individuals might hesitate to receive additional doses, potentially improving our understanding of the COVID-19 pandemic and possible responses to pandemics and other public health emergencies in the future.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".