The refusal of COVID-19 vaccination and its associated factors: a meta-analysis
Bibliographic record
Abstract
Background To date, more than 10% of the global population is unvaccinated against the coronavirus disease 2019 (COVID-19), and the reasons why this population is not vaccinated are not well identified. Objectives We investigated the prevalence of COVID-19 vaccine refusal and to assess the factors associated with COVID-19 vaccine refusal. Methods A meta-analysis was conducted from August to November 2022 (PROSPERO: CRD42022384562). We searched for articles investigating the refusal of COVID-19 vaccination and its potential associated factors in PubMed, Scopus, and the Web of Sciences. The quality of the articles was assessed using the Newcastle–Ottawa scale, and data were collected using a pilot form. The cumulative prevalence of the refusal to vaccinate against COVID-19 was identified through a single-arm meta-analysis. Factors associated with COVID-19 vaccine refusals were determined using the Mantel-Haenszel method. Results A total of 24 articles were included in the analysis. Our findings showed that the global prevalence of COVID-19 vaccine refusal was 12%, with the highest prevalence observed in the general population and the lowest prevalence in the healthcare worker subgroup. Furthermore, individuals with a high socioeconomic status, history of previous vaccination, and a medical background had a lower rate of COVID-19 vaccination refusal. Subsequently, the following factors were associated with an increased risk of COVID-19 vaccine refusal: being female, educational attainment lower than an undergraduate degree, and living in a rural area. Conclusion Our study identified the prevalence of and factors associated with COVID-19 vaccine refusal. This study may serve as an initial reference to achieve global coverage of COVID-19 vaccination by influencing the population of COVID-19 vaccine refusal.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".