Exploring the perspectives and strategies of Ontario community pharmacists to improve routine follow-up for patients with diabetes: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Medication reviews are a fundamental activity carried out as part of comprehensive care delivered by pharmacists. Varying programs that reimburse pharmacists for conduct of medication reviews are in place in different jurisdictions in Canada and other countries around the world. The MedsCheck Diabetes (MCD) program is a publicly funded service in Ontario, Canada, offered to patients with type 1 or type 2 diabetes. Through this service, pharmacists can complete a focused medication review with advice, training, monitoring and follow-up diabetes education. Although pharmacists can be reimbursed for patient follow-up activities, a low number of follow-up medication reviews are billed through this program. METHODS: The study explores the barriers and facilitators that community pharmacists in Ontario experience in conducting routine monitoring and follow-up of patients with diabetes. Using a descriptive content analysis approach study, semistructured interviews were conducted with a convenience sample of 8 community pharmacists working in Ontario. RESULTS: Three main themes emerged: the design of the MCD program, the state of community pharmacy and collaboration and relationships. These themes demonstrate challenges and potential strategies recognized by community pharmacists to conduct routine diabetes follow-up through the MCD program. CONCLUSION: This study found that the design of the MedsCheck Diabetes program, the community pharmacy environment and the relationships between pharmacists, patients and prescribers can pose a challenge in the conduct of routine monitoring and follow-up through the MedsCheck Diabetes program.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".