Exploring Pharmacists Attitudes, Practices, and Barriers to Conducting Medication Reviews in Community Pharmacy Settings
Bibliographic record
Abstract
Background: Medication review (MR) services are essential in optimizing therapy outcomes. In addition to the barriers hindering its adoption, understanding community pharmacists’ perception and practice of MR is crucial in formulating strategies to enhance MR services in pharmacy practice Objective: To evaluate pharmacists' attitudes, practices, and barriers to providing MR services in the community pharmacy setting. Methods: In this cross-sectional study, a validated self-administered questionnaire was distributed online to community pharmacists across the UAE using convenience-sampling technique. The questionnaire evaluated whether the pharmacists practiced MR, attitudes toward MR, the extent of its implementation, and the associated barriers. Stepwise regression was conducted to explore the variables associated with attitudes and MR practice. Results: The majority of the pharmacists (72.5%) reported providing MR, with a notable decline in engagement among older pharmacists (OR = 0.243, 95%Cl (0.113-0.522), employee pharmacists (OR = 0.909, 95% Cl (0.844-0.979) and those with no access to patient medication records (OR = 0.131, 95%Cl (0.052-0.327). The median attitude score was 27 out of 35, indicating favorable perceptions, particularly among pharmacists in chain pharmacies (OR = 2.974, 95%Cl (1.245-7.103). Employee pharmacists showed decreased odds of being in the high-attitude group (OR = 0.233, 95%Cl (0.076-0.714) and decreased odds to practice MR (OR = 0.192, 95%Cl (0.051-0.723) compared to pharmacists in charge or owner pharmacists. On the other hand, an increased number of working pharmacists (OR = 2.053, 95%Cl (1.381-3.052) and improved attitudes increased the odds to practice MR in the community pharmacy (OR = 2.617, 95%Cl (1.159-5.906). Conclusions: The study revealed a room for improvement in MR practice, especially among older and employee pharmacists. Enhancing the accessibility to specialized databases and authorized references, and enforcing pharmacists' attitudes towards MR practice via providing education and facilitating inter-professional collaboration are essential strategies to improve MR practice in the community pharmacy setting.
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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 | 0.004 | 0.014 |
| 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) | 0.001 | 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".