COVID-19 vaccine acceptance in sub-Saharan African countries: A systematic review and meta-analysis
Bibliographic record
Abstract
Vaccination is the most effective intervention for the primary prevention of COVID-19. Several studies have been conducted in sub-Saharan African countries on the acceptance and associated factors of COVID-19 vaccine. This review and meta-analysis aimed to recapitulate the pooled magnitude of vaccine acceptance and its favoring factors in sub-Saharan African countries. PUBMED, MEDLINE, Science Direct, Web of Science, and SCOPUS were the main databases searched from 15 March to 5 June 2022; and all the articles written in the English language were included. Also, some articles were retrieved from biomedical peer-reviewed journal sites and Google scholar. The quality of thirty-five selected articles was evaluated using an adapted scale for evaluating cross-sectional studies based on the Newcastle-Ottawa Scale. The result of the review and meta-analysis revealed that COVID-19 vaccine acceptance rate varied across studies. In a pooled analysis, factors such as; higher-level perception of infection risk (OR (95% CI (2.7 (2.1, 3.4))), perceived vaccine safety (13.9 (9.2, 20.9)), virus-related good knowledge (2.7 (2.3, 3.2)) and appropriate attitude (5.9 (4.4, 7.8)), adherence to safety precautions (5.5 (4.8, 6.2)), and infection experience (4.4 (2.8, 6.9)) were positively affected the COVID-19 vaccine acceptance. Also, vaccine acceptance was found to be high among males and chronically ill individuals. Thus, understanding factors that enhance vaccine acceptance would support planners to augment vaccine uptake in the region.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".