Prevalence of COVID-19 Vaccine Hesitancy among Healthcare Workers in Nigeria: A Systematic review and meta-analysis
Bibliographic record
Abstract
Vaccine hesitancy, particularly among health care workers, is a global health concern. Health care workers' refusals to be vaccinated against a virulent microbe may lead to a shortage of frontline workers when they are most needed. Objective: This systematic review and meta-analysis determined the prevalence of COVID-19 vaccine hesitancy among Nigerian health care workers. Methods Eligibility criteria: Only studies that reported hesitancy to COVID-19 vaccines among health care workers in Nigeria were included. Information sources: An extensive language-unrestricted literature search was conducted across PubMed, Scopus, the Cochrane Library, the African Index Medicus and gray literature. Risk of bias: The methodological quality of each included study was determined using a 10-point New Castle Ottawa scale that was modified for cross-sectional studies. Synthesis method: A single-arm meta-analysis was performed using a random-effects model. Results Of the 206 articles, 22 publications involving 20,724 participants were included. The pooled prevalence of COVID-19 vaccine hesitancy was found to be 75% (95% CI: 61-88%, I2 = 99.69%, P < 0.001). Reasons for hesitancy, including concerns about side effects, lack of trust, and safety, were prevalent at 76% (CI: 0.57 – 0.94, I2 = 99.24%, P < 0.001), 55% (CI: 0.042 - 0.272, I2 = 97.42%, P < 0.001), and 68% (CI: 0.047 - 0.89, I2 = 98.59%, P < 0.001), respectively. Discussion Limitation: Most primary studies had a high risk of bias in terms of conduct, comparability, and outcome measurement. Interpretation: The rate of morbidity and mortality among health care workers could rise, potentially affecting the health systems’ efforts to contain the virus. Conclusion There was significant hesitancy among Nigerian healthcare workers toward COVID-19 vaccines; thus, strategies to increase vaccination acceptance among health care workers should be developed. . Funding: This research was not funded. Registration number. CRD42022365489 is the PROSPERO registration number. Date: October 17, 2022.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".