Bibliographic record
Abstract
Purpose – The aim of this paper is to argue that quality alone is not enough; pharmacies need to develop a rapport with their customers and convey sincerity. Community (retail) pharmacy is an increasingly competitive industry. Service quality has been considered one of the key factors for pharmacies to differentiate and develop competitive advantage. The customers’ perception of the sincerity of the pharmacies mediates the relationship between quality and trust. Design/methodology/approach – Through consumer intercept, we collected survey data from 150 customers in a western Canadian city who had filled a prescription at the pharmacy in the previous six months. Data were analyzed using structure equation modeling. Findings – Our data revealed that while overall service quality has a positive influence on customer trust, perception of sincerity has a more immediate impact. The relationship between quality and trust was completely mediated through sincerity perception. Originality/value – Service quality has been introduced to pharmacy management and produced many good results. However, the measurement of service quality has historically emphasized on physical evidence. The element of human interaction and subjective affective perceptions has been largely ignored because it is difficult to measure, hence difficult to reward and implement. Our study highlights the importance of personal interactions in the context of pharmacy quality management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".