Knowledge, attitudes and barriers to uptake of COVID-19 vaccine in Uganda, February 2021
Bibliographic record
Abstract
INTRODUCTION: Uganda planned to start its COVID-19 vaccination campaign in March 2021, prioritising healthcare workers, security personnel, elderly and people with comorbidities. However, the willingness to receive the vaccine and potential barriers and hindrances were unknown. To understand the barriers to uptake of the COVID-19 vaccine prior to its rollout, we explored the communities' knowledge, attitudes and barriers. METHODS: We conducted a mixed-methods cross-sectional study in Kampala and Ankole subregions in February 2021. For the household survey, we used three-stage sampling to select three districts in each subregion and, thereafter, 12 villages per district. One adult in each household was interviewed. Additionally, we conducted focus group discussions and key informant interviews to explore knowledge, attitudes and barriers to COVID-19 vaccination. Modified Poisson regression was used to identify factors associated with willingness to receive to COVID-19 vaccine RESULTS: Among 1728 respondents, 52% were under 40 years old, and 67% were female. Fifty-nine percent of those who had heard of the vaccine primarly obtained information from radio and television (TV). Despite one-quarter reporting that they had heard that the vaccine could cause death or genetic changes, 85% were willing to receive it. Persons in the Kampala subregion were less willing than those in the Ankole subregion to take the vaccine (76% vs 94%, adjusted prevalence ratio (aPR)=0.85, 95% CI: 0.81 to 0.89). Trust in the effectiveness of non-vaccine COVID-19 preventive measures (aPR=0.89, 95% CI: 0.80 to 0.99), living in urban areas (aPR=0.84, 95% CI: 0.76 to 0.91) and lack of information on vaccine safety (aPR=0.91, 95% CI: 0.83 to 0.93) reduced interest in taking the vaccine. CONCLUSIONS: Vaccine willingness was high despite some misinformation and safety concerns, which more prevalent in Kampala than in the Ankole subregion. While radio and TV were major sources of COVID-19 vaccine information, social media was the biggest propagator of COVID-19 vaccine misinformation. Therefore, providing credible information about vaccine safety could reinforce uptake, especially among urban residents. Additionally, local and national leaders should publicise their acceptance of vaccines and debunk misinformation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".