Patterns of drug-related problems and the services provided to optimize drug therapy in the community pharmacy setting
Bibliographic record
Abstract
Introduction: Drug-related problems (DRPs) are events or circumstances involving drug therapy that actually or potentially interferes with desired health outcomes. Objectives: To assess community pharmacists' knowledge and practice regarding DRP-reduction services, as well as the barriers and factors associated with decreased provision of these services. Methods: This cross-sectional study utilized a validated questionnaire to assess pharmacists' knowledge, practice, and barriers to the provision of DRP-reduction services in the community pharmacy setting. Binary regression model was used to assess the variables associated with the practice of DRP-reduction services. Results: A total of 412 pharmacists participated in the study. The pharmacists demonstrated strong knowledge but inadequate practice of DRP-reduction services. The most reported DRPs were inappropriate combination of drugs, or drugs and herbal medications, or drugs and dietary supplements (52.4%), patients' inability to understand instructions properly (46.1%), inappropriate drug according to guidelines (43.7%), and too high dose (40.3%). The most common barriers to these services were increased workload (60.5%), limited time (53.2%), and lack of good communication skills (49.8%). The presence of a counselling area in the pharmacy increased the practice of DRP-reduction services (OR: 3.532, 95%Cl: 2.010-5.590, P < 0.001), while increased weekly working hours (OR: 0.966, 95%Cl: 0.947-0.986), P < 0.01) and serving < 10 patients daily (OR = 0.208, 95%Cl: 0.072-0.601, P < 0.01) decreased it. Conclusions: Community pharmacists' practice of DRP-reduction services showed a scope for improvement. Future pharmaceutical care initiatives should increase the number of personnel working in the pharmacy and provide them with opportunities for continued education and training in order to improve the provision of DRP services and optimize patients' 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".