Does Continuity of Community Pharmacy Care Influence Adherence to Statins
Bibliographic record
Abstract
Background: Improving adherence to medication is a persistent challenge within the health system. Adherence is influenced by many factors at the patient, provider, treatment and health system levels. Adherence may also be affected by continuity of care; defined as the consistent professional relationship between a health provider or source of care and a patient. \nObjective: To estimate the strength of association between continuity of community pharmacy care and adherence to statin medication among persons initiating statin therapy in Nova Scotia between 1998 and 2008. \nMethods: This was a retrospective cohort study using administrative data from the Nova Scotia Seniors’ Pharmacare program. Subjects were included if they were dispensed at least one prescription for a statin medication between 1998 and 2008. Continuity of care was calculated via two methods: the Usual Provider of Care (UPC) index and the Continuity of Care Index (COCI), which measure the density and dispersion of relational continuity of care, respectively. Adherence was calculated using the medication possession ratio. The strength of association between continuity of care and adherence was analyzed using hierarchical regression.\nResults: During the study period, 31 592 individual subjects received a first statin dispensation. Adjusted hierarchical regression showed that for each 0.10 increase in continuity of care, the odds of adherence increase by 3% (95% CI: 1.01-1.05). Continuity of care measured by the UPC is highly correlated with continuity of care measured by the COCI (r=0.98).\nConclusions: Continuity of community pharmacy care is positively associated with adherence to statins among Nova Scotian seniors who initiated statin therapy between 1998 and 2008.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".