Patient-pharmacist relationship dynamics: a mediation analysis of patient characteristics and reported outcomes
Bibliographic record
Abstract
Background: While previous research underscores the independent effect of the pharmacist-patient relationship on patient outcomes, it did not delve further into the patient-pharmacist relationship dynamics and their effects on reported outcomes. Therefore, this study aimed to assess whether patient-pharmacist relationship aspects mediate the association between patient personal and health characteristics, on the one hand, and adherence to medication and quality of life, on the other hand (QOL). Methods: An online cross-sectional study was conducted between April 11 and 27, 2023. It enrolled 865 adults from all Lebanese governorates and used validated scales to measure the various concepts. Results: The mean age was 32.52 ± 14.56 years, and 68.8% were female. Also, 79.3% reported having no chronic disease, and 57.7% indicated that getting nonprescription medications was the main reason for visiting a community pharmacy. The average routine intake of medications per day was 0.87 ± 1.78. Our key findings reveal a compelling association between worse health status and both increased medication non-adherence and reduced QOL. Sociodemographic factors were found to be correlated with QOL. Despite the considerable impact of demographic factors on patient expectations, our study challenges the expected mediation role of the pharmacist-patient relationship and counseling time on medication adherence. Nevertheless, patient expectations partially mediated the relationship between sociodemographic characteristics and QOL. Conclusion: This study sheds light on the intricate dynamics between patient characteristics, health status, medication adherence, and QOL within the context of the patient-pharmacist relationships.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".