TRUSTING THE PHARMACIST IN DELIVERING MEDICATION INFORMATION: A COMMUNITY-BASED PERSPECTIVE
Bibliographic record
Abstract
Objective: Optimal disease management is influenced by a solid patient-health provider relationship; which includes trust in the provider. The study compares respondents’ trust in pharmacists and physicians for the delivery of drug information. Methods: Residents of 3 rural communities in Lebanon, aged 40 and above, were invited to participate in the study, 760 accepted. Participants were asked who they trust the most with information about their medication: their physician or their pharmacist. Results: Of the total sample, 154 chose the pharmacist as their most trusted source of medication information (20%). Characteristics associated with choosing the pharmacist were: being a male (29.3% vs 16.2% p<.001), of younger age (31.5% among<50 y, 18.8% among 50-64 y, and 14.6% among 65+years p<.001), single (31.6% vs 21.9% married and 9.3 others, p=0.023), working (39.2% vs15.7% p<.001), and insured (2.3% vs 16.4% p=0.048). The multivariate logistic regression model revealed that having a family member with hypertension (OR=1.86 95% 1.23-2.82), or cardiovascular (OR=3.39 95%CI 1.55-7.45) increased the likelihood of trusting pharmacists over medical doctor. On the other hand, a self-report of cardiovascular disease (OR=0.34 95% CI 0.12-0.95) and taking medication (OR=0.41 95% CI 0.25-0.67) were associated with a decrease in the trust in the pharmacist in favor of the physician. Conclusion: Although pharmacists are the drug specialists, the majority of the Lebanese rural community residents reported higher trust in their physicians with information about their medication(s).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".