Systematic Review and Meta-analysis on COVID-19 Vaccine Hesitancy
Bibliographic record
Abstract
Abstract Background: The presented meta-analysis was developed in response to the publication of several studies addressing COVID-19 vaccines hesitancy. We aimed to identify the proportion of vaccine acceptance and rejection, and factors affecting vaccine hesitancy worldwide especially with the fast emergency approval of vaccines. Methods: Online database search was performed, and relevant studies were included with no language restriction. A meta-analysis was conducted using R software to obtain the random effect model of the pooled prevalence of vaccine acceptance and rejection. Egger’s regression test was performed to assess publication bias. Quality assessment was assessed using Newcastle-Ottawa Scale quality assessment tool. Results: Thirty-nine out of 12246 articles met the predefined inclusion criteria. All studies were cross-sectional designs. The pooled proportion of COVID-19 vaccine hesitancy was 17% (95% CI: 14-20) while the pooled proportion of COVID-19 vaccine acceptance was 75% (95% CI: 71-79). The vaccine hesitancy and the vaccine acceptance showed high heterogeneity (I 2 =100%). Case fatality ratio and the number of reported cases had significant effect on the vaccine acceptance as the pooled proportion of vaccine acceptance increased by 39.95% (95% CI: 20.1-59.8) for each 1% increase in case fatality (P<0.0001) and decreased by 0.1% (95% CI: -0.2-0.01) for each 1000 reported case of COVID-19, P= 0.0183). Conclusion: Transparency in reporting the number of newly diagnosed COVID-19 cases and deaths is mandatory as these factors are the main determinants of COVID-19 vaccine acceptance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".