Association Between Loyalty to Community Pharmacy and Medication Persistence and Compliance, and the Use of Guidelines-Recommended Drugs in Type 2 Diabetes
Bibliographic record
Abstract
Pharmacists record data on all drugs claimed and may build a personal relationship with their clients. We hypothesized that loyalty to a single pharmacy could be associated with a better quality of drug use.To assess the association between pharmacy loyalty and quality of drug use among individuals treated with oral antidiabetes drugs (OADs).This is a cohort study using Quebec Health Insurance Board data. Associations were assessed using multivariable logistic regression.New OAD users, aged ≥18 years.Individuals who filled all their prescription drugs in the same pharmacy during the first year of treatment were considered loyal. During year 2 of treatment we assessed 4 quality indicators of drug use: persistence with antidiabetes treatment, compliance with antidiabetes treatment among those considered persistent, use of an angiotensin-converting enzyme inhibitor or of an angiotensin II receptor blocker (ACEi/ARB), and use of a lipid-lowering drug.Of 124,009 individuals, 59.75% were identified as loyal. Nonloyal individuals were less likely to persist with their antidiabetes treatment (adjusted odds ratio = 0.89; 95% CI: 0.86-0.91), to comply with their antidiabetes treatment (0.82; 0.79-0.84), to use an ACEi/ARB (0.85; 0.83-0.88) and to use a lipid-lowering drug (0.83; 0.80-0.85). Quality of drug use decreased as the number of different pharmacies increased (linear contrast tests <0.001).Results underscore the important role pharmacists could play in helping their clients with chronic diseases to better manage their drug treatments. Further research is needed to determine to what extent the positive effects associated with pharmacy loyalty are specifically due to pharmacists.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".