Attitude Towards COVID-19 Vaccination Among Healthcare Workers: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Availability and accessibility of a safe COVID-19 vaccine do not necessarily guarantee an effective means to mitigate the pandemic. However, the fragile hero's or health care worker's attitude toward the vaccine is of paramount importance to promote its acceptance. So, the current review aims to provide the latest assessment of healthcare workers' attitudes toward the COVID-19 vaccination and its contributing factor worldwide. METHODS: Peer-reviewed surveys in English indexed via an electronic database in Google Scholar, Science Direct and PubMed were systematically searched. The review was carried out per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA-2009) and registered on PROSPERO (CRD42021265534). RESULTS: Originally 8039 articles were searched from three databases PubMed, Science direct, and Google scholar. Finally, 24 studies met the inclusion criteria and made the root for the estimates of the attitude of COVID -19 vaccinations. In about two-thirds of the studies, respondents showed a positive attitude (≥50%) toward COVID-19 vaccination. However, in about one-quarter of the studies, a negative attitude (<50%) against vaccination was reported. Factors related to the attitude of healthcare workers toward COVID-19 vaccination include age, sex, profession, concerns about the safety of vaccines and fear of COVID-19, trust in the accuracy of the measures taken by the government, flu vaccination during the previous season, comorbid chronic illness, history of recommendation, and depression symptoms in the past week. CONCLUSION: Although most studies report that healthcare workers have a positive attitude toward COVID-19 vaccination, quite a few surveys mention negative attitudes towards the use of vaccines, which may reflect missed opportunities or challenges for the international efforts aimed at mitigating the pandemic. Still, we need to continue to make more efforts to change the attitudes of the uncertain healthcare workers to increase the uptake of the vaccine and deal with the multi-faceted impact of infection.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".