Global acceptance and rejection of COVID-19 vaccines: A systematic review and meta-analysis
Bibliographic record
Abstract
A challengeable obstacle to the introduction of new vaccine that affects the transmission of certain infections is vaccine hesitancy, despite the availability of vaccines. To assess the theoretical tendencies and public attitudes concerning the COVID-19 vaccinations. PsycINFO, Science Direct, Embase, Scopus, EBSCO, MEDLINEcentral/PubMed, ProQuest, SciELO, SAGE, Web of Science, and Google Scholar were searched. All papers detailing rejection and acceptance of the COVID-19 vaccine were included with no language restriction. Abstracts, proposals, conferences, editorials, author responses, reviews, case reports & series, books, and studies with data not accurately extracted or overlapping data were excluded. A meta-analysis was conducted using the random effect model of the pooled proportion of vaccine acceptance and rejection using the meta-package of R software. Egger’s regression test was performed to assess publication bias, and the quality of included studies was assessed using the Newcastle-Ottawa Scale. Out of 12246 identified records, 36 articles were included in the quantitative analysis. The pooled proportion of COVID-19 vaccine rejection was 16% (95%CI:13-20, I2=100%), while that of COVID-19 vaccine acceptance was 65% (95% CI:60-70, I2=100%. Case-fatality ratio and geographical distribution represented the main determinants of vaccine acceptance. Vaccine acceptance increased by 27.17% (95% CI:3.46-50.88) for each 1% increase in case fatality (p<0.02). The acceptance increased in Africa by 1.86 (p=0.04) while the vaccine rejection decreased in Australia by 3.93(p<0.0001). This meta-analysis demonstrated poor acceptance of COVID-19 vaccines, and the ratio of cases to fatalities had a profound effect on public perception of the vaccines. These findings should be used to inform relevant interventions for future pandemic responses.PROSPERO registration-number:CRD42021232805
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".