Does the type of pharmacy used influence medication adherence? A retrospective observational study in Aotearoa, New Zealand
Bibliographic record
Abstract
Background: Community pharmacies in New Zealand have varying ownership and operational structures. Unlike independent pharmacies, corporate and hybrid pharmacies do not charge prescription copayments. Objectives: This research aimed to determine whether people receiving free prescriptions from corporate and hybrid pharmacies (via copayment waiver) have greater medication adherence than the users of independent pharmacies. Methods: A nationwide, retrospective, observational study linked 1 year of dispensing data (1/05/2022 to 30/04/2023) from the Pharmaceutical Collection to patient enrollment data using a National Health Index number to identify demographics of different pharmacy-type users. People were assigned to a particular type of pharmacy if they collected at least 70% of their prescriptions from there; if they did not meet this threshold, they were defined as mixed users. People were classified as adherent if dispensing data showed they collected their supply of medication to cover at least 80% of the study period. Results: The sample captured 218,080 people taking at least 1 diabetes medication, with a total of 360,079 unique medications being included in the analysis. The majority, 156,893, used independent pharmacies. The type of pharmacy used was shown to be a significant predictor of adherence. Corporate and hybrid pharmacy users were 0.90 (95% CI 0.88 to 0.93) and 0.93 (95% CI 0.90 to 0.96) times as likely be adherent than the users of independent pharmacies. Mail order pharmacy users were the most likely to be adherent, whereas mixed pharmacy users were the least likely to be adherent. Conclusions: Our findings suggest that prescription copayments provided by corporate and hybrid pharmacies are not the most significant barrier to medication adherence. Further research may identify more efficient ways of improving medication adherence than removing prescription copayments for all.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".