Determinants of Covid-19 Vaccine Acceptance among Students: A Web-Based Global Survey
Bibliographic record
Abstract
Background: Acceptance of a COVID-19 vaccine is crucial to achieve sufficient immunization coverage to end the pandemic. After initially focusing on adults, the emphasis of vaccination is now being geared towards the younger generation. In order to mandate vaccines in schools and attain widespread vaccine uptake, it is important to understand the key determinants that influence students’ willingness to receive a COVID-19 vaccine. Hence, this study was designed to explore students’ willingness to receive a vaccine, their concerns regarding vaccination, and additional factors influencing COVID-19 vaccine acceptance. Method: Descriptive analytic cross-sectional study using snowball and convenience sample technique was conducted from July - September 2021. Social media networks such as Twitter, WhatsApp and Instagram were used. Data from the student population of both genders receiving secondary and post-secondary education was collected from the Asia-Pacific, Middle East, Europe, and America (26-countries from all over the world). Descriptive statistics and Chi square tests were used. Multivariate logistic regression analysis was used to determine significant predictors for vaccine acceptance. Results: A total of 201 participants completed the questionnaire (response rate 53%). We found considerably higher willingness (85%) to take a COVID-19 vaccine in the sample; highest among students in the West (95.0%), followed by Asia-Pacific region (84.0%) and the least among Middle East (80.0%). A statistically significant association (p = 0.000) was found between the female gender and the willingness for vaccine receival. Preserving health [OR 18.82, 95%CI 2.88-122.80], understanding the importance of vaccinations for protection against COVID 19 [OR 34.28, 95%CI 3.72-315.95], concerns about vaccine safety [OR 1.77, 95%CI1.21-28.78] and worry about potential side effects [OR 0.027, 95%CI 0.004-0.213] were significant predictors for vaccine acceptance. Conclusion: The majority of students were willing to get the COVID-19 vaccine to protect their health; but there were concerns about safety and side effects. Greater understanding about the importance of the vaccine, for protection against COVID-19 was predictive of willingness to receive the vaccine. This study provided evidence for health authorities to provide clear information, reduce misinformation and design measures to address the fears and worries about the effects of the vaccine. Future qualitative studies should be directed towards understanding differences in students’ perspectives in depth.
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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 | 0.037 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".