COVID-19 Vaccine Acceptability: A Cross-Sectional Mixed Methods Study in Sierra Leone
Bibliographic record
Abstract
Abstract Background Global immunization is critical to combat the COVID-19 pandemic and the public's willingness to be vaccinated will determine the success of elimination efforts. We measured the acceptability of COVID-19 vaccine and views in Sierra Leone. Results Most survey respondents (80.1%, n = 1719) would accept COVID-19 vaccination for themselves and close family, but 19.9% (n = 427) would reject vaccination. If vaccination was mandatory the acceptance rate would increase to 85.0%, (n = 1,823), while 15.0% (n = 318) of responders would still reject. COVID-19 vaccine awareness was high among respondents (yes: 75.2% n = 1613, no: 24.8%, n = 533). Safety, immunity, and trust in vaccines were the main reasons for vaccine acceptance. Distrust, uncertainties about vaccine safety and effectiveness and lack of belief in the COVID-19 pandemic triggered through media reports were the main reasons for rejecting the COVID-19 vaccination. With respect to vaccine, a small majority would prefer a less reactogenic vaccine even at the cost of a lower efficacy over a more effective but more reactogenic vaccine (55.7%, n = 1195 vs 41.5%, n = 890) while 2.8% (n = 61) of respondents said they would reject any vaccine. Country of origin had an important role in vaccine acceptance: 32.4% (n = 1121) would accept a vaccine from any country if licensed locally, but 15.1% (n = 511) would reject vaccines even if licensed from China,12.6% (n = 437), India 11.4% (n = 393), USA 8.0% (n = 276), Germany 7.8% (n = 271), Russia 7.7% (n = 267), UK and 5.0% (n = 173) from Belgium. Conclusion Sensitizing the public about the COVID-19 infection risk, vaccine development processes and ensuring vaccine safety through continuous communication and community engagement, with community leaders leading by example as well as the independent role of regulatory authorities in safety and efficacy evaluation, would improve COVID-19 vaccine acceptance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".