COVID-19 vaccine acceptance and associated factors in 13 African countries: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The COVID-19 pandemic has severely affected the entire world, especially sub-Saharan Africa. As a result, researchers and government agencies are working to create effective COVID-19 vaccinations. While vaccination campaigns are moving rapidly in high-income nations, COVID-19 is still ruthlessly affecting people in low-income nations. However, this difference in the spread of the disease is not because of a lack of a COVID-19 vaccine but mainly due to people's reluctance. As a result, this review summarized the data on COVID-19 vaccination adoption and factors related among nations in sub-Saharan Africa. Method: Comprehensive searches were conducted using PubMed, Embase, Medline, Web of Science, Google Scholar, and the Cochrane Library databases. The risk of bias and methodological quality of each published article that fit the selection criteria were evaluated using Critical Appraisal Checklist tools. All statistical analysis was done by STATA 16. Results: = 99.55%. Being male [POR = 1.88 (95% CI: 1.45, 2.44)], having a positive attitude toward the COVID-19 vaccine [POR = 5.56 (95% CI: 3.63, 8.51)], having good knowledge in the COVID-19 vaccine [POR = 4.61 (95% CI: 1.24, 8.75)], having government trust [POR = 7.10 (95% CI: 2.37, 21.32)], and having undergone COVID-19 testing in the past [POR = 4.41 (95%CI: (2.51, 7.75)] were significant predictor variables. Conclusion: This analysis showed that respondents had a decreased pooled prevalence of COVID-19 vaccination acceptance. Sex, attitude, knowledge, government trust, and COVID-19 testing were statistically significantly correlated characteristics that affected the acceptability of the COVID-19 vaccine. All stakeholders should be actively involved in increasing the uptake of the COVID-19 vaccine and thereby reducing the consequences of COVID-19. The acceptance of the COVID-19 vaccination can be increased by using this conclusion as an indicator for governments, healthcare professionals, and health policymakers in their work on attitude, knowledge, government trust, and COVID-19 testing.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".