COVID-19 vaccine acceptance among health care workers in Africa: A systematic review and meta-analysis
Bibliographic record
Abstract
Coronavirus Disease (COVID-19) vaccine acceptance, and hesitancy amongst Health Care Workers (HCWs) on the African continent have been examined through observational studies. However, there are currently no comprehensive reviews among these cadre of population in Africa. Hence, we aimed to review the acceptance rate and possible reasons for COVID-19 vaccine non-acceptance/hesitancy amongst HCWs in Africa.We searched Medline/PubMed, Google Scholar, and Africa Journal Online from January, 2020 to September, 2021. The Newcastle-Ottawa Quality Assessment tool adapted for cross-sectional studies was used to assess the quality of the retrieved studies. DerSimonian and Laird random-effects model was used to pool the COVID-19 vaccine acceptance rate. Sub-group and sensitivity analyses were performed. Reasons for COVID-19 vaccine hesitancy were also systematically analyzed.Twenty-one (21) studies were found to be eligible for review out of the 513 initial records. The estimated pooled COVID-19 vaccine acceptance rate was 46% [95% CI: 37%-54%]. The pooled estimated COVID-19 vaccine acceptance rate was 37% [95% CI: 27%-47%] in North Africa, 28% [95% CI: 20%-36%] in Central Africa, 48% [CI: 38%-58%] in West Africa, 49% [95% CI: 30%-69%] in East Africa, and 90% [CI: 85%-96%] in Southern Africa. The estimated pooled vaccine acceptance was 48% [95% CI:38%-57%] for healthcare workers, and 34% [95% CI:29%-39%] for the healthcare students. Major drivers and reasons were the side effects of the vaccine, vaccine's safety, efficacy and effectiveness, short duration of the clinical trials, COVID-19 infections, limited information, and social trust.The data revealed generally low acceptance of the vaccine amongst HCWs across Africa. The side effects of the vaccine, vaccine's safety, efficacy and effectiveness, short duration of the clinical trials, COVID-19 infections, limited information, and social trust were the major reasons for COVID-19 hesitancy in Africa. The misconceptions and barriers to COVID-19 vaccine acceptance amongst HCWs must be addressed as soon as possible in the continent to boost COVID-19 vaccination rates in Africa.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 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) | 0.004 | 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".