A Global Map of COVID-19 Vaccine Acceptance Rates per Country: An Updated Concise Narrative Review
Why this work is in the frame
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Bibliographic record
Abstract
The delay or refusal of vaccination, which defines vaccine hesitancy, is a major challenge to successful control of COVID-19 epidemic. The huge number of publications addressing COVID-19 vaccine hesitancy necessitates periodic review to provide a concise summary of COVID-19 vaccine acceptance rates worldwide. In the current narrative review, data on COVID-19 vaccine acceptance rates were retrieved from surveys in 114 countries/territories. In East and Southern Africa (n = 9), the highest COVID-19 vaccine acceptance rate was reported in Ethiopia (92%), while the lowest rate was reported in Zimbabwe (50%). In West/Central Africa (n = 13), the highest rate was reported in Niger (93%), while the lowest rate was reported in Cameroon (15%). In Asia and the Pacific (n = 16), the highest rates were reported in Nepal and Vietnam (97%), while the lowest rate was reported in Hong Kong (42%). In Eastern Europe/Central Asia (n = 7), the highest rates were reported in Montenegro (69%) and Kazakhstan (64%), while the lowest rate was reported in Russia (30%). In Latin America and the Caribbean (n = 20), the highest rate was reported in Mexico (88%), while the lowest rate was reported in Haiti (43%). In the Middle East/North Africa (MENA, n = 22), the highest rate was reported in Tunisia (92%), while the lowest rate was reported in Iraq (13%). In Western/Central Europe and North America (n = 27), the highest rates were reported in Canada (91%) and Norway (89%), while the lowest rates were reported in Cyprus and Portugal (35%). COVID-19 vaccine acceptance rates ≥60% were seen in 72/114 countries/territories, compared to 42 countries/territories with rates between 13% and 59%. The phenomenon of COVID-19 vaccine hesitancy appeared more pronounced in the MENA, Europe and Central Asia, and Western/Central Africa. More studies are recommended in Africa, Eastern Europe and Central Asia to address intentions of the general public to get COVID-19 vaccination.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it