COVID-19 vaccine acceptance and its associated factors in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
Background COVID-19 vaccination is considered as an effective intervention for controlling the burden of the pandemic. However, vaccine hesitation is increasing and hindering efforts targeting to reduce the burden of the COVID-19 disease. Hence, determining COVID-19 vaccine acceptance and identifying determinants that would hinder people to vaccinate against COVID-19 is crucial to effectively improve COVID-19 vaccine uptake. In Ethiopia, the pooled proportion of COVID-19 vaccine acceptance and its determinants is not well known. Thus, the aim of this study is to estimate the pooled proportion of COVID-19 vaccine acceptance and its determinants in Ethiopia. Methods A systematic search of articles was conducted from PubMed, Scopus, Web of Science, MEDLINE, CINAHL, Science Direct and Cochrane Library. Data were extracted using a data extraction tool which was adapted from the Joanna Briggs Institute. The quality of each included primary studies was evaluated using the Newcastle-Ottawa scale tool. Data analysis was performed using STATA 14. Heterogeneity in studies was assessed using Cochrane Q and I 2 test. Publication bias was assessed using visual inspection of funnel plots and Egger's test. A random effects model was applied to determine the pooled estimates if heterogeneity was exhibited; otherwise, a fixed-effects model was used. Results A total of 14 studies involving 6373 participants were included for the final analysis. The pooled proportion of COVID-19 vaccine acceptance in Ethiopia was 56.02% (95% CI: 47.84, 64.20). The likelihood of COVID-19 vaccine acceptance was higher among participants who had history of chronic disease (AOR = 1.33, 95% CI: 1.09, 2.97), good knowledge (AOR = 2.13, 95% CI: 1.59, 4.97), positive attitude (AOR = 2.23, 95% CI: 1.21, 4.66), good COVID-19 preventive practice (AOR = 1.97, 95% CI: 1.82, 2.12), and high perceived seriousness of COVID-19 (AOR = 3.21, 95% CI: 2.32, 5.98). Conclusion More than half participants were willing to accept COVID-19 vaccine. Thus, awareness creation battles about the efficacy and safety of the COVID-19 vaccine should be provided to the community. Besides, policy-makers, health planners and other stakeholders should encourage COVID-19 vaccine uptake behaviors by providing trusted information. Systematic review and meta-analysis registration : PROSPERO CRD42021264708.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".