Uptake of the MedsCheck annual medication review service in Ontario community pharmacies between 2007 and 2013
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: MedsCheck Annual (MCA) is an Ontario government-funded medication review service for individuals taking 3 or more prescription medications for chronic conditions. METHODS: This cohort study analyzed linked administrative claims data from April 1, 2007, to March 31, 2013. Trends in MCA claims and recipient characteristics were examined. RESULTS: A total of 1,498,440 Ontarians (55% seniors, 55% female) received an MCA. One-third (36%) had 2 or more MCAs within 6 years. Service provision increased over time, with a sharper increase from 2010 onward. Almost half of Ontario pharmacies made at least 1 MCA claim in the first month of the program. Hypertension, respiratory disease, diabetes, psychiatric conditions and arthritis were common comorbidities. Recipients older than 65 years were most commonly dispensed an antihypertensive and/or antihyperlipidemic drug in the prior year and received an average of 11 unique prescription medications. Thirty-eight percent of recipients visited an emergency department or were hospitalized in the year prior to their first MCA. DISCUSSION: Over the first 6 years of the program, approximately 1 in 9 Ontarians received an MCA. There was rapid and widespread uptake of the service. Common chronic conditions were well represented among MCA recipients. Older MCA recipients had less emergency department use compared with population-based estimates. CONCLUSIONS: Medication reviews increased over time; however, the number of persons receiving the service more than once was low. Service delivery was generally consistent with program eligibility; however, there are some findings possibly consistent with delivery to less complex patients.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it