Assessing the determinants of uptake and hesitancy in accessing COVID 19 vaccines in Nigeria: a scoping review
Bibliographic record
Abstract
The coronavirus disease (COVID-19) is one of the largest public health threats in recent times, with significant health, economic, and social consequences globally. The WHO reported that over 651 million cases and 6.6 million deaths were attributed to COVID-19 globally. The Nigeria Centre for Disease Control (NCDC) in 2022 revealed that 266,057 cases with 3,155 deaths were reported. All the thirty-six states and the Federal Capital Territory (FCT) of Nigeria were affected, but Lagos and the FCT reported the highest number of cases. However, it is possible that these numbers do not accurately reflect the severity of COVID-19 disease in Nigeria because the country had only tested 5,160,280 people as at 2022, despite a population of around 200 million. Nigeria did not meet its 2021 vaccination target, prompting the need to identify the contextual factors affecting vaccine access and uptake as well as vaccine hesitancy in Nigeria and document the approaches that can be deployed to reduce opposition to vaccination as well as improve advocacy for vaccine equity. This scoping review, conducted using Arksey and O'Malley's framework, aimed to explore the factors influencing COVID-19 vaccine hesitancy and uptake in Nigeria. A comprehensive literature search was conducted across electronic databases, including Google Scholar and PubMed, with studies from Nigeria published in English. The review included 25 studies on vaccine hesitancy, uptake, and willingness to accept COVID-19 vaccination, identifying barriers at the national, community, and individual levels. The results indicated that 90% of the studies showed low vaccine acceptance and uptake, with barriers related to vaccine availability, misinformation, cultural and religious influences, socioeconomic factors, and lack of trust in the health system. Socio-demographic factors such as gender, age, education, and income were identified as key influences. The findings highlight the need for targeted, evidence-based strategies to address vaccine hesitancy, improve vaccine distribution, and engage diverse population groups to enhance vaccination uptake across 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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".