Assessment of Knowledge, Perceptions, and Attitudes During the Global Mpox Outbreak in June 2022: A Cross-Sectional Study From the United Arab Emirates
Bibliographic record
Abstract
Objectives: To examine knowledge, worry, anxiety, and vaccine acceptance for mpox among UAE adults. Methods: An online survey, advertised on academic and social media platform in June 2022 collected data from 959 participants (aged 18 and above) on mpox beliefs, risks, knowledge, worry, anxiety, COVID-19 infection, vaccination, and willingness to receive the mpox vaccine. Bivariate and logistic regression analysis identified associations and predictors between variables. Results: 56% had optimal knowledge of mpox transmission and symptoms. 54% were worried, and 27% experienced anxiety related to the outbreak. Knowledge scores were higher among women, healthcare workers, and those with reliable information sources. High perceived infection risk, changes in precautionary measures, and belief in difficult treatment predicted more worry and anxiety. Higher worry and two or more doses of the COVID-19 vaccine predicted higher likelihood of taking the mpox vaccine. Conclusion: The UAE population showed low knowledge and high worry and anxiety during the global mpox outbreak. Increasing public awareness through targeted educational campaigns is vital. Promoting better understanding of infectious diseases, addressing concerns, and encouraging vaccine uptake can prepare for future outbreaks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".