ArticleHeliyon2023
COVID-19 vaccination attitudes across the European continent.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- A qualitative exploration of deterrents to COVID-19 vaccination uptake among adults in post-war Tigray, Northern Ethiopia.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was conducted to determine the predictors of COVID-19 vaccination attitudes across multiple waves in seven countries geographically spread across the European continent, using data from a COVID-19 survey provided by the Massachusetts Institute of Technology COVID-19. Facebook users from across the globe participated in this survey which collected information on their knowledge of COVID-19, attitudes towards risk and available information, and their willingness or lack thereof to take the vaccine. In this secondary data analysis study, neural networks were used with special attention given to the importance of the predictors of COVID-19 vaccination attitudes. Perception of social norms regarding COVID-19 vaccination was found to be the most important predictor of vaccine acceptance. Country of residence and wave of data collection were among the important predictors, with different patterns for each country emerging across different waves. Other strong predictors included attitudes towards masks and mask wearing; attitudes towards the influenza vaccine; distrust in government health authorities and scientists; and level of knowledge of existing treatments for COVID-19. The results of this study can inform effective public health prevention and intervention efforts against infectious diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.