Determinants of COVID-19 Vaccine Uptake among Boda-Boda Riders in Mathare, Kenya
Bibliographic record
Abstract
Coronavirus disease (COVID-2019) is a novel, highly infectious disease rapidly spreading worldwide. Kenya began the immunization campaign at the beginning of March 2021 with AstraZeneca vaccines, and by June 2022, a total of 18.5 million doses of the coronavirus vaccine had been administered. Due to low uptake of the Coronavirus Disease 2019 vaccine, the Kenyan government called for prioritizing interventions to improve vaccine uptake. This study examined the influence of perception, prior exposure to coronavirus, policy, individual factors, and access to Coronavirus Disease 2019 vaccines on uptake of COVID-19 vaccination among Boda-Boda cyclists in Mathare Sub-County, Nairobi. This was a cross-sectional study with a sample size of 138 Boda-Boda cyclists. The data were purely quantitative and were collected using a structured questionnaire. Simple random sampling was used to select respondents. Data were analyzed using SPSS version 21, and both descriptive and inferential statistics (chi-square, logistic regression) were used. The study found that only a quarter (35 [25%]) of respondents had been vaccinated, of whom 84 (60%) had received the first dose and 56 (40%) had received the required 2 doses. A third (47 [33.6%]) of vaccinated respondents preferred the Johnson & Johnson vaccine. Regarding how policies affected uptake of the COVID-19 vaccine, 97 (69.3%) disagreed that they got vaccinated because they wanted to receive services from government offices. Regarding vaccine accessibility, 78 (55.7%) of respondents agreed that they could get vaccinated at their local health facilities and at any time. The coefficients associated with individual factors, perceptions, policies, and vaccine accessibility were statistically significant (P < .05). The study recommends conducting more educational campaigns on the illness and the need for vaccination.
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How this classification was reachedexpand
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".