The refusal of COVID-19 vaccination and its associated factors: a systematic review
Bibliographic record
Abstract
<ns3:p>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.</ns3:p> <ns3:p>Objectives: We investigated the prevalence of COVID-19 vaccine refusal and to assess the factors associated with COVID-19 vaccine refusal.</ns3:p> <ns3:p>Methods: A meta-analysis was conducted during August to November 2022 (PROSPERO: CRD42022384562). We searched 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 the data were collected using a pilot form. The cumulative prevalence of the refusal to vaccinate against COVID-19 was identified using a single-arm meta-analysis. The factors associated with COVID-19 vaccine refusals were determined using the Mantel-Haenszel method.</ns3:p> <ns3:p>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%, and the highest prevalence was in the general population, and the lowest prevalence was in the healthcare worker subgroup. Furthermore, individuals with a high socioeconomic status, history of previous vaccination, and 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.</ns3:p> <ns3:p>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.</ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".