Why Vaccinate Against COVID-19? A Population-Based Survey in Switzerland
Bibliographic record
Abstract
Following the approval of the first COVID-19 vaccines by Swissmedic in December 2020, vaccination campaigns began across Switzerland (1). In Ticino, the Italian speaking canton that borders the heavily affected regions in northern Italy, vaccinations began in January 2021 prioritizing older adults and frontline healthcare and social workers (2). In the first quarter of 2021, Switzerland administered, free of charge at the point of delivery (3), the mRNA vaccines from Pfizer-BioNTech and Moderna (4). In mid-March 2021, when the present study was carried out, the 14-day incidence of confirmed cases per 100,000 inhabitants at the national level was 194. In Ticino, this number ranged from 240 to 479 cases (5). The availability of COVID-19 vaccines is crucial for protection and to reduce the risk of severe disease through adequate immunization coverage. However, availability alone is not sufficient to achieve these aims (6–10). Vaccination decisions are influenced by several interacting drivers, including emotional, cultural, social, religious, logistical, political, and cognitive factors (11–13). The COVID-19 pandemic entails additional and unique challenges for public confidence in vaccines (14, 15). The development of vaccines was exceptionally fast, data on both safety and efficacy is, to date, short-term. Its use was initially authorized under emergency use terms (EUA) (16), and the composition, functioning and technology of most COVID-19 vaccines are relatively novel (17). Furthermore, concerns about the Oxford-AstraZeneca and Johnson and Johnson vaccine in other countries triggered safety concerns and confusion in the public (14, 18, 19). Another considerable challenge is posed by the infodemic associated with the pandemic and the unprecedented spread of misinformation, which does not spare vaccines (20–22). These factors contribute to vaccine hesitancy, defined as a “delay in acceptance or refusal of vaccination despite availability of (safe and efficacious) vaccines” (11) (p. 4163). Global COVID-19 vaccination acceptance was ≥70%, but with marked geographic variations (23). Intention to get vaccinated ranged from 28% in Congo to 93% in China during the first year of the pandemic (24, 25). Lower levels of education and working in the healthcare sector were associated with lower intention (26). Some personality traits and attitudes, having received an influenza vaccination in the last year, and perceived threat to physical health were all associated with greater intention (23, 27). Serological testing has become more common and accessible through large serosurveys used to measure the extent of the COVID-19 infection in populations. However, little is known about the potential modulating effect of known prior infection on vaccination intention. Callaghan et al. (28) found that past infection with COVID-19 was negatively correlated with vaccine uptake. Exposure to the virus conceivably confers protection against re-infection and/or some level of functional immunity against secondary severe COVID-19 disease (29). Therefore, knowledge of immune memory from primary infection(s) may lead to a lower intent to vaccinate against COVID-19. Moreover, evidence on vaccination intention in older adults, who are more prone to develop severe symptoms, and children, for whom vaccines were approved in December 2021, is extremely sparse, but very important (30). Evidence on vaccination intention in teenagers and children, who are often asymptomatic carriers (31), is crucial to inform public health decisions, and may contribute to attaining herd immunity (32, 33).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.226 | 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 teacher head, 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".