Mpox vaccine acceptance among healthcare workers: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Mpox is a zoonotic viral disease that emerged in May 2022 and has since shown a high prevalence in non-mpox-endemic areas, resulting in an outbreak that caused more than 84,000 cases in 110 countries around the globe. Several vaccines are available to prevent the disease, and multiple studies have been conducted to assess the attitudes of different populations toward receiving the mpox vaccine. This study systematically reviews all the studies conducted on mpox vaccine acceptance/hesitancy among healthcare workers. METHODS: A systematic literature search was conducted through four electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar, up to March 2023. Studies that described mpox vaccine acceptance/hesitancy among healthcare workers were included, and the data were extracted using a uniform extraction sheet. Following the extraction, the meta-analysis included ten studies with 7322 healthcare workers. Three researchers independently assessed the risk of bias in the included study using the Newcastle-Ottawa Scale (NOS). RESULTS: Ten studies were included in the review. This review indicates that the prevalence of mpox vaccine acceptance was 58.5%, and the prevalence of mpox vaccine hesitancy was 41.5%. There was a higher prevalence of acceptance in countries located in Asian and African areas compared to those in North America and Europe, estimated at 68% and 44.3%, respectively. Among the studies conducted solely among physicians, there was a high prevalence of mpox vaccine acceptance, at 77.1%, compared to 49% in studies that included all healthcare workers. CONCLUSION: There is a significant variation in the prevalence of mpox vaccine acceptance among different populations. Further research is needed to identify the factors that contribute to this variation and to develop interventions to increase vaccine acceptance. In addition, it is important to promote research on mpox vaccine acceptance and hesitancy among healthcare workers in countries where data is limited. This research will help policymakers develop effective policies to increase acceptance and reduce the disease burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| 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".