Factors Influencing Public Attitudes and Willingness to Utilize Telepharmacy Services in the UAE
Bibliographic record
Abstract
Background: Telepharmacy, utilizing telecommunications to dispense pharmaceutical products and deliver patient care, offers numerous benefits for both the public and pharmacists. Previous research on exploring attitudes and willingness to use telepharmacy services has primarily focused on pharmacists rather than the general population. Aim: This study is aimed at assessing the attitudes and willingness of the United Arab Emirates (UAE) population to utilize telepharmacy services and identifying the factors influencing their inclination to use these services. Methods: In this cross‐sectional study, a survey was distributed using convenience and snowball sampling to individuals aged 18 or older across the UAE through various social media platforms, including Twitter, Facebook, and WhatsApp. The survey domains included sociodemographics, attitudes, and readiness to utilize a telepharmacy service. A binary logistic regression analysis was conducted to investigate the variables associated with participants’ willingness to utilize telepharmacy in the future. Results: In total, 963 individuals participated in the study. Participants showed overall positive attitudes towards telepharmacy, with 70.9% believing that telepharmacy saved time and effort. While only 32% of the participants acknowledged that numerous telepharmacy services were available for use in the UAE, most were interested in using telepharmacy services in the future (79.2%). Participants who had higher attitude scores (AOR = 1.147, 95% confidence interval [CI]: 1.11–1.18) and those who had used these services previously (AOR = 3.270, 95% CI: 1.692–6.320) were more interested in using telepharmacy services in the future. Conclusion: Forthcoming healthcare strategies should focus on expanding the availability of telepharmacy services throughout various regions of the country. This expansion will facilitate the broader utilization of these services and ultimately contribute to improved health outcomes.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".