What drives the willingness to get vaccinated against COVID-19 in South Africa?
Bibliographic record
Abstract
The willingness to get vaccinated in South Africa is among the highest in the world, measuring at 76%. This study investigated the impact of individual risk beliefs, self-reported health status, and familiarity with someone with coronavirus disease 2019 (COVID-19) on the willingness to get vaccinated in South Africa. Data were obtained from the Wave 5 of the South African National Income Dynamics Study – Coronavirus Rapid Mobile Survey. Data were analyzed using descriptive statistics and binary logistic regression. More than 53% of the population believed that they were not at risk of COVID-19; 71.8% believed that they were in good health; and 31.6% knew someone with COVID-19. Beliefs (odds ratio [OR]: 1.287), health status (OR: 1.064), and COVID-19 case familiarity (OR: 1.034) were associated with willingness to get vaccinated. Other associations remained positive in the adjusted model. The relationship between case familiarity and willingness to get vaccinated shows that knowing someone who died of COVID-19 or suffered from the discomfort induced by the disease may drive other individuals to get vaccinated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".