How People in Eight European Countries Felt About the Safety, Effectiveness, and Necessity of COVID-19 Vaccination: A Cross-Sectional Survey
Bibliographic record
Abstract
Background/objectives: Attitudes towards COVID-19 vaccination vary globally, influenced by political and cultural factors. This research aimed to assess the views of people without a healthcare qualification in Europe on COVID-19 vaccination safety, effectiveness, and necessity as well as how well informed they felt. The secondary outcomes focused on how respondents’ views were affected by demographic and context factors and included a comparison by country of the level of feeling well informed. Methods: A mixed-method cross-sectional online survey in eight European countries, using convenience sampling. Results: A total of 1008 adults completed the survey, 60% of whom were female. While only 44.1% considered the vaccines safe, 43.5% effective, and 44.9% necessary, 80.0% had been vaccinated. Four in ten adults strongly agreed that they were well informed, while over a quarter did not answer the question. Younger respondents, well-informed individuals, and German respondents were more inclined to perceive COVID-19 vaccination as both effective and necessary. Conclusions: Motivations for vaccination included perceived health and social benefits, while concerns included a preference for “natural immunity”, the rapid development of the vaccine, and potential unknown long-term effects. A correlation existed between respondents feeling well informed about the different COVID-19 vaccines in their country and the likelihood of having been vaccinated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".