COVID-19: Vaccine Hesitancy in Africa and the Way Forward
Bibliographic record
Abstract
COVID-19 pandemic took the world by storm in late 2019, scientists and health authorities across the globe struggle to contain the deadly virus. Socio-economic activities across the globe were partly halted as countries around the world introduce various forms of restrictions to contain the spread of the COVID-19 virus. Most developing countries’ economies, especially in Africa, slid into recession, unemployment among Africa countries skyrocketed to an all-time high, and famine and starvation were beginning to knock harder on poorer nations around the world. The race to develop a vaccine was pressing harder; developed countries continue to pump more money to help develop a vaccine within the shortest period of time, as that seems the only viable solution to the economic downturn of the global world. Finally, vaccines were developed and proved to have high efficacy. This has helped reverse the negative trend of the global economy caused by the COVID-19 pandemic. This vaccine faced a lot of global scrutinies, but many people have refused to get vaccinated and have also rejected the idea of making COVID-19 vaccination compulsory for citizens worldwide. This study analyzes the challenges posed by this ugly trend of COVID-19 vaccine hesitancy in African countries, its socio-economic consequences and the way forward.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".