Registry Systems for COVID-19 Vaccines and Rate of Acceptability for Vaccination Before and After Availability of Vaccines in 12 Countries: A Narrative Review
Bibliographic record
Abstract
Registry systems play a key role in promoting vaccination campaigns in the general population. In the present narrative review, we provide data from 12 12 countries for vaccination acceptance before the availability of COVID-19 vaccines and vaccination coverage once it is available. We selected a randomized representative sample of 12 countries from WHO regions and 194 total members by the Open Epi Random Program. We observed the results with different levels of vaccine acceptability between the studies that were performed before the availability of a vaccine against COVID-19 and the vaccination coverage after the availability of the COVID-19 vaccine. All the registry systems that were developed for the recent pandemic achieved the initial functional goals. Twelve months after the vaccination campaign has begun, varying results were reported for vaccination coverage against COVID-19 vaccines with rates as high as 98% (subjects with at least one dose of vaccine) in the United Arabic Emirates, and as low as 24% in South Africa. The United Arabic Emirates stood as the leader of the world with the highest number of vaccinations 88% fully vaccinated citizens followed by Canada with 80% fully vaccinated citizens. The available data suggest that vaccine registry systems could help increase vaccination coverage and aim in the control of future outbreaks.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".