Community pharmacists’ knowledge, attitudes, and concerns about POCT in the UAE: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Point-of-care testing (POCT) is increasingly recognized for improving timely diagnosis and disease management. Community pharmacists are well-positioned to support POCT delivery, particularly in underserved settings. This study assessed the knowledge, attitudes, and concerns of community pharmacists in the UAE regarding POCT implementation. METHODS: A cross-sectional survey was conducted among 501 licensed community pharmacists across the UAE using convenience sampling. A validated questionnaire measured pharmacists' knowledge (19 items), attitudes (9 items), and concerns toward POCT. Quantile regression was applied to identify predictors of knowledge and attitudes. RESULTS: The median knowledge score was 8/19 (IQR: 3); only 20.4% recognized POCT use in infectious disease detection, and 21.8% were aware of regulatory requirements. The median attitude score was 8/9 (IQR: 3); 66.4% emphasized the need for communication of POCT results to patients and providers, while 64.7% highlighted the importance of mastering POCT mechanics. Main concerns included accuracy (72.3%) and sample collection (60.9%). Female pharmacists and those dispensing more prescriptions daily had higher knowledge scores, whereas older pharmacists showed lower attitude scores. CONCLUSIONS: Although attitudes toward POCT were generally positive, notable knowledge gaps and concerns persist.
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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 | 0.002 | 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) | 0.002 | 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".