Patients’ views and experiences of the first community pharmacy-based medication therapy management clinic in the Middle East and North Africa (MENA): A qualitative study
Bibliographic record
Abstract
Objective: This qualitative study aimed to describe patients' experiences of a community pharmacy (CP)-based medication therapy management program (MTM). Methods: Qualitative, semistructured, face-to-face interviews were conducted with a purposive sample of patients with uncontrolled diabetes who received care at a CP-based MTM clinic. Interviews were conducted in the MTM clinic of Health Kingdom CP in Riyadh City, Kingdom of Saudi Arabia by a research pharmacist using an interview guide. Data collection was continued until data saturation. All interviews were audiorecorded, transcribed verbatim, and analyzed thematically. Key findings: A total of 16 patients, of whom more than half were male, were interviewed between October 2021 and March 2022. The mean ± standard deviation age of the patients was 52.0 ± 8.9 years, whereas the mean number of years since the first diagnosis of diabetes was 11.2 ± 7.3 years. Three main themes emerged from the interviews: perceived benefits and outcomes of the program, factors driving positive patient experiences, and challenges and recommendations for enhancing MTM service. Generally, patients were satisfied with the quality of advanced care that they received at the clinic and recognized the importance of the pharmacist's role. Furthermore, the program was perceived by patients as an opportunity to transition to a healthier lifestyle. Patients also highlighted a few barriers related to follow-up, such as accessibility, and issues with the service, such as long waiting times. Finally, there were some suggestions for patient improvement. These include expanding the clinic space, initiating educational and follow-up messages, and cooperating with other specialists as required. Conclusions: Patients received the MTM program very positively with noticeable health benefits. Barriers to effective follow-up and care should be addressed before implementing this service in wider community pharmacies in Saudi Arabia and beyond.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".