The appointment-based model in community pharmacies: Patient demographics and reimbursable clinical services uptake in Ontario
Bibliographic record
Abstract
Background: Community pharmacies typically require patients to request medication refills. The appointment-based model (ABM) is a proactive approach that synchronizes refills and schedules patient-pharmacist appointments. These appointments provide opportunities for medication reviews, medication optimization and health promotion services. The primary aim of this study was to describe the types of patients who received an ABM service in a community pharmacy in Ontario in 2017. The secondary aim was to describe reimbursable clinical service uptake. Methods: In September 2017, the ABM was implemented across 3 Ontario community pharmacies within a Canadian pharmacy banner. Patients who filled at least 1 chronic oral medication and consented to enrolment were eligible. In December 2018, data were extracted from pharmacies using pharmacy management software. Descriptive statistics and frequencies were generated. Results: Analysis of 131 patients (51.1% female; mean ± SD age 70.8 ± 10.5 years) revealed patients were dispensed a mean ± SD of 5.1 ± 2.7 medications, and 73 (55.7%) experienced polypharmacy. Hypertension (87.8%) and dyslipidemia (68.7%) were the most common medical conditions. There were 74 (56.5%) patients who received ≥1 medication review service (MedsCheck). Of 79 unique drug therapy problems (DTPs) identified, the most common categories related to patients needing additional drug therapy and adverse drug reactions. Discussion and conclusion: Patients enrolled in the ABM were generally older adults experiencing polypharmacy. The ABM presented opportunities for DTP identification and delivery of reimbursed services. Findings support continued exploration of the ABM to support integration of clinical services within community practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".