Measuring Vaccine Literacy in Switzerland: How is the situation after COVID-19?
Bibliographic record
Abstract
Abstract Background Vaccinations are one of the most effective medical health interventions and one of the most important preventive measures. However, the population is often unsure about vaccination, and the roles and responsibilities of the key actors seem often unclear. To adequately protect the population against vaccine-preventable diseases, the Swiss Confederation, the Cantons and other stakeholders have developed the National Vaccination Strategy and an associated action plan. For its implementation, studies on the population’s knowledge, attitudes, skills, and vaccination behavior are crucial. Thus, the Swiss Federal Office of Public Health launched a study to assess vaccine literacy and its relation to vaccination readiness and behavior in the adult population in Switzerland. Methods A representative sample of 2,500 people living in Switzerland will be surveyed in summer 2024. The online questionnaire will include validated instruments, e.g. the HLS19-VAC Instrument to measure vaccine literacy or the 7C Vaccination Readiness Scale, as well as questions on correlates of vaccine literacy, vaccination readiness and behavior. Results The results of the survey will be presented, with a focus on vaccine literacy, vaccination readiness and behavior and their correlates. Specifically, differences among the three main language regions and between specific population groups will be shown. Further, a comparison with data from previous studies will be presented. Conclusions Despite having an advanced healthcare system, to date Switzerland has only partially reached its objectives in terms of vaccination, both for individual protection and collective immunity. New data on vaccine literacy, vaccination readiness and behavior as well as on the influencing factors are needed, especially after the pandemic, to guide effective future actions and measures to improve the vaccine coverage and public health in Switzerland. Key messages • Due to the COVID-19 pandemic the topic of vaccination in general has received increasing attention and may has changed people’s knowledge and attitudes towards vaccination. • Newest data on vaccine literacy, vaccination readiness and behavior are necessary to inform future, targeted actions and increase vaccine uptake.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".