Vaccination literacy, readiness and behavior in Switzerland: a representative national study
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has led to an increased attention towards vaccination and has emphasized the critical role of vaccination literacy (VL). However, it has remained unclear, how this societal and health crisis has influenced VL, i.e., the attitudes, knowledge, and information-seeking behavior of the Swiss adult population regarding vaccination, their vaccination readiness and behavior in general, and the challenges they encounter in this domain. This study investigates these issues to inform targeted public health interventions. Methods Building on a previous study (Schulz et al., 2019), a mixed-methods approach using a representative online survey (n = 2,076) in the three main Swiss languages, followed by 30 in-depth, semi-structured online interviews to explore participants’ perspectives and experiences, was applied in 2024. Results The quantitative results show that 51% of the Swiss population have low VL (MV: 72.1 on a scale of 0-100). Respondents reported great challenges in distinguishing between misinformation and reliable information (61% found it (very) difficult) and in assessing trustworthiness (63% found it (very) difficult). Higher VL is associated with greater vaccination knowledge, greater vaccination readiness and a positive perception of vaccination benefits. In addition, individuals with a stronger social network tend to have greater vaccination knowledge and VL. The qualitative findings confirm that information acquisition is a major challenge, largely due the presence of conflicting, ambiguous or non-transparent information. Conclusions Promoting VL requires clear, evidence-based communication. Public authorities should be transparent about the benefits and potential risks of vaccination, while acknowledging different views to build trust. In addition, patient-centered communication by health professionals is essential to provide a supportive space to address uncertainties and respond to individual concerns.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".