Exploring vaccine hesitancy among healthcare providers in the United Arab Emirates: a qualitative study
Bibliographic record
Abstract
Healthcare providers (HCPs) are at the frontline to curb the spread of vaccine hesitancy in the community. However, HCPs themselves may delay or refuse vaccines. In light of the emerging vaccine hesitancy in the United Arab Emirates (UAE), we aimed to explore HCPs doubts and concerns regarding vaccination. We conducted face-to-face interviews with 33 HCPs from 7 ambulatory healthcare services in the Al Ain region, UAE. An interview guide was developed based on the European Center for Disease Prevention and Control guide for vaccine hesitancy among HCPs. An inductive thematic framework was employed to explore the main and emerging themes conceptualizing the predisposing, reinforcing, and enabling factors that influence HCPs' hesitancy regarding vaccinations for themselves and while recommending, prescribing, or discussing vaccines with their patients. The sample included general practitioners, family physicians, nurses, pharmacists, and other administrative staff. The major themes included positive predisposing factors such as trust in the system and the government, previous education, and social responsibility. Positive enabling factors included affordability and availability of vaccination services. Many participants were hesitant to receive the mandatory influenza vaccination. Misinformation regarding vaccines on social media was a major concern. However, HCPs showed little interest in being active on social media. Most participants reported never receiving any training on how to address vaccine hesitancy among patients. Because HCPs play an important role in influencing patients' decisions regarding undergoing vaccination, their confidence in addressing vaccine hesitancy must be improved.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".