Acceptance of coronavirus disease 2019 (COVID-19) vaccines among healthcare workers: A meta-analysis
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has posed increasing challenges to global health systems. Vaccination against COVID-19 can effectively prevent the public, particularly healthcare workers (HCWs), from being infected by this disease. Objectives: We aim to understand the factors influencing HCWs' acceptance of COVID-19 vaccines. Methods: We searched PubMed, Embase and Web of Science to collect literature published before May 15, 2022, about HCWs' acceptance of COVID-19 vaccines. The Newcastle-Ottawa quality assessment scale was used to assess the risk of bias and the quality of the included studies. We utilized Stata 14.0 software for this meta-analysis with a random-effects model, and odds ratios (ORs) with 95% confidence intervals (CIs) were reported. This meta-analysis was conducted in alignment with the preferred reporting items for systematic review and meta-analysis (PRISMA) guideline. Results: Our meta-analysis included 71 articles with 93,508 HCWs involved. The research showed that the acceptance of vaccines had significantly increased among HCWs compared to non-HCWs (OR = 1.91, 95% CI: 1.16-3.12). A willingness to undergo COVID-19 vaccination was observed in 66% (95% CI: 0.61-0.67) of HCWs. Among the HCWs involved, doctors showed a generally increased intention to be vaccinated compared with nurses (OR = 2.22, 95% CI: 1.71-2.89). Additionally, males were found to hold more positive attitudes toward vaccination than females (OR = 1.81, 95% CI: 1.55-2.12). When the effectiveness of COVID-19 vaccines was improved, the vaccination acceptance of HCWs was greatly increased accordingly (OR = 5.03, 95% CI: 2.77-9.11). The HCWs who were willing to vaccinate against seasonal influenza showed an increased acceptance of COVID-19 vaccines (OR = 3.52, 95% CI: 2.34-5.28). Our study also showed that HCWs who were willing to be vaccinated against COVID-19 experienced a reduced rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (OR = 0.78, 95% CI: 0.66-0.92). Conclusions: Our analysis revealed that the five factors of occupation, gender, vaccine effectiveness, seasonal influenza vaccines, and SARS-CoV-2 infection presumably affected the acceptance of COVID-19 vaccines among HCWs. It is essential to boost the confidence of HCWs in COVID-19 vaccines for the containment of the epidemic.
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.076 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".