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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".