Awareness, Familiarity, and Pharmacist Trust: A Structural Equation Model Analysis
Bibliographic record
Abstract
Background: Trust in health care professionals is critical in the health care system and is needed for a patient to seek care, reveal sensitive information, and follow a specified treatment plan, among other things. Objective: To better understand trust in community pharmacists, this research develops a model of how patient awareness of the different community pharmacy roles (role awareness) and pharmacist familiarity influences pharmacist trust. Methods: A survey of pharmacy patients in Nova Scotia, Canada, occurred in November and December 2019, with quota sampling used to achieve representativeness by age, gender, and household income. A total of 640 usable surveys were obtained. Consistent partial least squares was deployed to test and refine the model. Results: Overall, the final model highlights that both role awareness and pharmacist familiarity influence patient assessments of pharmacist trust and explains 38.7% of its variance. Pharmacist familiarity has a stronger influence than role awareness on pharmacist trust. Results of the consistent partial least squares multigroup analysis found no statistically significant differences in the model based on patient gender. Conclusion: This research provides a means to capture interpersonal trust in community pharmacists and identifies 2 key determinants of such trust. This research also provides guidance on how to assess pharmacist trust, the value of patients knowing their pharmacist, and the value of patient awareness of the roles of the various professionals behind the counter. Such knowledge will help pharmacy managers, associations, and regulatory authorities develop evidence-informed plans to assess, rebuild, and sustain trust.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".