Evaluation of Pharmacy Model in a Trial of Free Essential Medicine Access
Bibliographic record
Abstract
Background: In Canada, pharmacists accessing electronic health records (EHR) and mailing medications to patients are relatively uncommon. We evaluated a pharmacy model implemented in a clinical trial that combined allowing the pharmacist access to patients’ EHR and mailing medications to participants. Methods: We conducted thematic analysis of comments made by participants and prescribers, and chart stimulated recalls with the pharmacist involved with the novel pharmacy model implemented in a clinical trial. Results: Major themes from participant’s comments related to the ease of obtaining information about medications from the pharmacy and satisfaction with the delivery. Prescribers felt that this model facilitated collaboration with the pharmacist and welcomed suggestions regarding therapeutic medication changes. Major themes from the pharmacist’s chart stimulated recalls were that access to participants’ EHRs allowed for improved drug therapy management and participant experience, and this pharmacy model increased participant’s access to pharmacy services. Discussion: According to the pharmacist and prescribers, this pharmacy model facilitated their collaboration in prescribing appropriate medications and participants were generally satisfied with the delivery of medications. Conclusion: Participants and prescribers were generally supportive of a pharmacy model that combined allowing the pharmacist access to participants’ EHR and medication mailing. This allowed the pharmacist more opportunities for drug therapy management and collaboration with prescribers. It also improved the participant’s access to pharmacy services, although those services were not always fully utilized.
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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 | 0.074 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".