Covid-19 Vaccine Acceptance in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background The Coronavirus Disease-2019 pandemic impacted the everyday lives of the global population whereas a considerable proportion of people are offhand of taking the vaccine. And hence this study assessed the overall level of vaccine acceptance in Ethiopia. Methods Database search for articles was made systematically across Google Scholar, Web of Science, Science Direct, Hinari, EMBASE, Boolean operator, and PubMed. Selection, screening, reviewing, and data extraction was made by two reviewers independently using a Microsoft Excel spreadsheet. The modified Newcastle-Ottawa Scale(NOS) and the Joanna Briggs Institute prevalence critical appraisal tools were used to assess the quality of evidence. All studies conducted in Ethiopia, reporting vaccine acceptance were incorporated. The extracted data were imported into the Comprehensive meta-analysis version3.0 for further analysis. Publication bias was checked by using Beggs and Eggers tests. Heterogeneity was checked by Higgins’s method. A random-effects meta-analysis model with a 95% confidence interval was computed to estimate the pooled effect size (prevalence). Furthermore, subgroup analysis based on the study area and sample size was done
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".