Utilization, perceived benefits and concerns regarding robotic technologies among community pharmacists
Bibliographic record
Abstract
BACKGROUND: Robotic technology is being rapidly adopted worldwide. The purpose of this study was to quantify the prevalence of robotic technology use among UAE community pharmacists, evaluate their perceived benefits and concerns, and identify factors that predict heightened concern levels. RESEARCH DESIGN AND METHODS: The present study utilized a validated self-administered survey, which was distributed in person to community pharmacists in different regions of Abu Dhabi and other Emirates. The questionnaire comprised sociodemographic and job‑related items, an operational definition of robotics, a 5‑point Likert scale on perceived benefits, a 4‑point Likert scale on perceived concerns (recoded to a 0-14 score), and a checklist of potential robotic pharmacy services. RESULTS: Pharmacists holding only a bachelor's degree and pharmacy owners reported higher median concern scores than those with postgraduate degrees and pharmacists in charge, respectively. Additionally, pharmacists without training on robotic systems and those with heavier workloads dispensing ≥30 prescriptions per day or serving ≥10 patients per day also showed significantly greater concerns than their counterparts. CONCLUSION: It is necessary to implement training initiatives aimed at enhancing awareness and understanding of robotic technologies among pharmacists.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".