Prevalence and Drivers of COVID-19 Vaccine Hesitancy among Czech University Students: National Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: university students are believed to retain the highest levels of health literacy. They are perceived as the opinion leaders within their communities; therefore, their health-related beliefs and attitudes are deemed important for public health campaigns. This study aimed to investigate the COVID-19 vaccine hesitancy drivers among university students in the Czech Republic. METHODS: a cross-sectional study using a self-administered questionnaire was carried out in the weeks before the unrestricted vaccine deployment to Czech adults. The questionnaire had 21 multiple-choice items stratified in 4 categories; demographic characteristics, COVID-19-related anamnesis and influenza vaccine experience, attitudes towards COVID-19 vaccination, and the possible drivers of COVID-19 vaccine hesitancy suggested by the WHO-SAGE. RESULTS: out of the 1351 included students, 66.8% were females, 84.5% were Czech nationals, and 40.6% enrolled in healthcare programs. The overall COVID-19 vaccine acceptance level was 73.3%, 19.3% of participants were vaccine-resistant, and only 7.4% were vaccine-hesitant. Trust in the pharmaceutical industry, trust in healthcare providers, and perceived knowledge sufficiency predicted higher odds of vaccine acceptance. In contrast, media and social media, personal beliefs, immunity misconception, previous COVID-19 infection, and suspicions about novel vaccines and the local availability predicted higher odds of vaccine hesitancy. CONCLUSIONS: The findings of this study predict a fair probability to achieve community immunity (herd immunity) among the target population group. The primary prevention strategies in the Czech Republic need to be culturally sensitive and inclusive for foreign nationals. As one-quarter of the participating students are dependent on vaccine safety data, this study findings support the call for independent studies evaluating the side effects of COVID-19 vaccines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".