Adult Vaccination in the United Arab Emirates—A Physicians' Knowledge and Knowledge Sources Study
Bibliographic record
Abstract
Background A lack of knowledge on adult vaccination has been documented among physicians. They play a critical role in promoting adult vaccines. This study aimed to review the status of adult vaccination in the United Arab Emirates (UAE) and evaluate physicians' knowledge and knowledge sources regarding adult vaccines. Methods Local, regional, and global adult vaccination guidelines were reviewed. A 40-item questionnaire was used to collect data from physicians from June to October 2020, using convenience and snowball sampling. Knowledge score was calculated, and predictors identified using Mann–Whitney U and Kruskal–Wallis H-tests. Ordinary Least Squares regression was used for Multivariate Analysis. Results A total of 500 responses were included. A quarter were internists, and another quarter were family physicians. Fifty-seven percent were medical interns and residents. Both perceived and actual knowledge of adult vaccination were low. Bivariate analysis showed knowledge depending on department, level of training, workplace, and perceived knowledge. All remained significant after multivariable regression except workplace. International and local guidelines were the most common knowledge sources. Forty-two percent were unable to access the local guidelines. Conclusions Physicians' knowledge was poor and local guidelines were not clear or easily accessible. Participants were highly receptive to guidance and practice with adult vaccines.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".