Predictors of COVID-19 Vaccine Hesitancy in North-Central Nigeria
Bibliographic record
Abstract
Abstract COVID-19 vaccine hesitancy has emerged as a major challenge to global efforts to control the pandemic, particularly in Nigeria, where hesitancy to other effective vaccines such as polio and measles has been widely reported. Several individual, societal, and structural factors contribute to this behaviour and prevent the effectiveness of COVID-19 prevention efforts. Objectives This study sought to assess the factors associated with COVID-19 vaccine hesitancy in the six states of north-central Nigeria. Methods A population-based cross-sectional online survey was conducted among residents using a semi-structured questionnaire adapted from the WHO SAGE vaccine hesitancy scale and distributed via social media networks over 8-weeks. Results A total of 1,999 responses were received, of which 570 were set aside comprising 512 respondents that resided outside the study area, 12 respondents that reported no knowledge of the COVID-19 vaccine, and 46 entries with missing data. Of 1,429 included in the analysis, 1,008 (70.5%) were willing to be vaccinated and/or already vaccinated and 421 (29.5%) were unwilling to receive the COVID-19 vaccine. Post-secondary education (AOR: 0.51, 0.37-0.69), household income above the minimum wage of 30,000 Naira per month (AOR: 0.68, 0.50-0.94) and people of the Islamic faith (AOR: 0.69, 0.53-0.90) were found to be associated with lower levels of hesitancy. The dominant reasons for hesitancy were concerns about side effects (37.5%), doubt about the existence of COVID-19 (11.0%), and the perception of time required to receive the vaccine (9.6%). Hesitant respondents relied on health workers (33.0%) and social media (23.3%) as their trusted sources of health information, and less than a third (31.4%) followed the advice of their religious and community leaders. Conclusion All three factors of confidence, complacency and convenience influenced hesitancy in our study. Socioeconomic factors are major drivers of hesitancy. Therefore, hesitancy is as much a social issue as health and requires a multisectoral approach to educating communities and building trust in health and social institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".