COVID-19 vaccine acceptance among healthcare workers in China: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Since the successful development of Coronavirus Disease (COVID-19) vaccine, COVID-19 vaccination has been actively advocated all over the world. As the key population for COVID-19 vaccination, the acceptance of Healthcare Workers (HCWs) is not only related to their risk of contracting COVID-19 infection at work, but also affects the decision of the general population on COVID-19 vaccination. Currently, a series of observational studies have been conducted on the acceptance of COVID-19 vaccines among HCWs in China, but there are presently no all-inclusive reviews. Therefore, this paper reviewed to identify a reliable estimate of acceptance rate of COVID-19 vaccine among HCWs in China. METHODS: We conducted a search on PubMed, EMbase, The Cochrane Library, Web of Science, CNKI (Chinese National Knowledge Infrastructure), Wanfang Database, CBM (Chinese Biomedical Literature Database) and VIP database (Chinese Scientific Journal Database) from January 2020 to June 2022. The quality of included articles was estimated using the Newcastle-Ottawa Quality Assessment tool suitable for cross-sectional studies and STATA 16 was used for analysis, A random-effects model was used to calculate acceptance rate for COVID-19 vaccine, as well as subgroup analysis and sensitivity analysis. RESULT: This review included 18 studies involving 45,760 subjects, all of which were of medium or high quality. Meta-analysis results represented that, the pooled estimated acceptance rate of COVID-19 vaccine among HCWs in China was 78% (95%CI: 73-83%), and the pooled acceptance rate in 2021 (82%, 95%CI: 78-86%) was significantly higher than that in 2020 (73%, 95%CI: 65%-81%). Subgroup analysis showed different acceptance rates for COVID-19 vaccine among HCWs with different characteristics. CONCLUSION: The result revealed that HCWs in China generally have a high acceptance rate of COVID-19 vaccines, but the acceptance rate varies with different characteristics of the population. Therefore, corresponding training should be carried out for HCWs with different characteristics, and they should play an exemplary and leading role in COVID-19 vaccination, so as to improve the vaccination rate of the whole population and form an immune barrier at an early date.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".