Factors affecting the delivery of community pharmacist-led medication reviews: evidence from the MedsCheck annual service in Ontario
Bibliographic record
Abstract
BACKGROUND: Medication reviews have become part of pharmacy practice across developed countries. This study aimed to identify factors affecting the likelihood of eligible Ontario seniors receiving a pharmacy-led medication review called MedsCheck annual (MCA). METHODS: We designed a cohort study using a random sample of pharmacy claims for MCA-eligible Ontario seniors using linked administrative data from April 2012 to March 2013. Guided by a conceptual framework, we constructed a generalized-estimating-equations model to estimate the effect of patient, pharmacy and community factors on the likelihood of receiving MCA. RESULTS: Of the 2,878,958 eligible claim-dates, 65,605 included an MCA. Compared to eligible individuals who did not receive an MCA, recipients were more likely to have a prior MCA (OR = 3.03), receive a new medication on the claim-date (OR = 1.78), be hypertensive (OR = 1.18) or have a recent hospitalization (OR = 1.07). MCA recipients had fewer medications (e.g., OR = 0.44 for ≥12 medications versus 0-4 medications), and were less likely to receive an MCA in a rural (OR = 0.74) or high-volume pharmacy (OR = 0.65). CONCLUSIONS: The most important determinant of receiving an MCA was having had a prior MCA. Overall, MCA recipients were healthier, younger, urban-dwelling, and taking fewer medications than non-recipients. Policies regarding current and future medication review programs may need to evolve to ensure that those at greatest need receive timely and comprehensive medication reviews.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".