COVID-19 vaccine hesitancy in Africa: A scoping review
Bibliographic record
Abstract
Abstract Background: Vaccination against the novel coronavirus is one of the most effective strategies for combating the global Coronavirus disease (COVID-19) pandemic. Vaccine hesitancy has however emerged as a major obstacle in several regions of the world, including Africa.Objective: To rapidly summarize the literature on COVID-19 vaccine hesitancy in Africa.Methods: We searched OVID Medline, Google Scholar, African Journals Online, and African Index Medicus for studies published from January 1, 2020, to July 5, 2021, examining acceptance or hesitancy towards the COVID-19 vaccine in Africa. Information on study characteristics study participants’ attitudes towards COVID-19 vaccine were extracted from the included articles. Factors associated with vaccine hesitancy or uptake were grouped as themes and summarized.Results: A total of 16 articles met the eligibility criteria and were included in the review. Majority of the studies were conducted in Nigeria and Democratic Republic of Congo. Studies conducted in Ghana, Somalia, Uganda, Benin, Cameroon, Malawi, Mali, Ethiopia, and South Africa were also included in the review. The vaccine acceptance rate ranged from 15.4 to 88.8 %. The major reasons for vaccine hesitancy were concerns with vaccine safety and side effects, lack of trust for pharmaceutical industries and vaccination trials, and misinformation or conflicting information from the media. Factors associated with positive attitudes towards the vaccine included being male, having a higher level of education, and fear of contracting the virus.Conclusion: Our review provides important considerations for addressing the challenge of COVID-19 vaccine hesitancy and blunting the impact of the pandemic in Africa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".