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Table1_Prevalence of COVID-19 Vaccine Hesitancy Among Healthcare Workers in Nigeria: A Systematic Review and Meta-Analysis.pdf

2025· dataset· en· W6908637283 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typedataset
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careVaccinationSystematic reviewScale (ratio)MEDLINEQuality (philosophy)Meta-analysis

Abstract

fetched live from OpenAlex

Objective The purpose of this systematic review and meta-analysis was to determine the prevalence of COVID-19 vaccine hesitancy among Nigerian healthcare professionals. Methods An extensive language-unrestricted literature search was conducted across PubMed, Scopus, the Cochrane Library, and the African Index Medicus to identify studies reporting hesitancy to COVID-19 vaccines among healthcare workers in Nigeria. Quality assessment was performed using the Newcastle-Ottawa scale for cross-sectional studies. A single-arm meta-analysis was performed using a random-effects model. Results Of the 206 articles, 22 publications with 20,724 participants were included. The pooled prevalence of COVID-19 vaccine hesitancy was 75% (95% CI: 61%–88%, I<sup>2</sup> = 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, I<sup>2</sup> = 99.24%, P < 0.001), 55% (CI: 0.042–0.272, I<sup>2</sup> = 97.42%, P < 0.001), and 68% (CI: 0.047–0.89, I<sup>2</sup> = 98.59%, P < 0.001), respectively. Conclusion There was significant hesitancy among Nigerian healthcare workers towards COVID-19 vaccination; thus, strategies to increase vaccination acceptance among healthcare workers should be developed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.517
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.5170.000

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.

Opus teacher head0.077
GPT teacher head0.297
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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