Predictors of the willingness to accept a free COVID-19 vaccine among households in Nigeria
Bibliographic record
Abstract
BACKGROUND: To inform vaccination policy and programmatic strategies to increase COVID-19 vaccine uptake, an understanding of the factors associated with the willingness to vaccinate is needed. METHODS: We analyzed data collected from the sixth and tenth round of the Nigerian COVID-19 National Longitudinal Phone Survey conducted by the National Bureau of Statistics and the World Bank in 2020 and 2021, respectively. Exploratory data analysis and feature selection techniques were used to identify important variables. Multivariable logistic regression models were fitted to assess the association between socio-demographic and economic factors and the willingness to receive a free COVID-19 vaccine among Nigerian households at two different time points before vaccines became widely available. RESULTS: : 0.32, 95% CI: [0.22, 0.48]) less likely to be willing to receive a free vaccine compared to households in North-Central Nigeria. CONCLUSION: These findings from two different time points before vaccine roll-out suggest that the educational level of household head, proportion of male household members, and the geopolitical zone of residence are important baseline predictors of the willingness to receive a free COVID-19 vaccine in Nigeria. These factors should be carefully considered and specifically targeted when designing public health programs to inform early-stage strategies that address underlying vaccine hesitancy, improve vaccine uptake, promote ongoing COVID-19 vaccination efforts, and potentially enhance other immunization programs in Nigeria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".