Monitoring and managing medication adherence in community pharmacies in Quebec, Canada
Bibliographic record
Abstract
Background: Community pharmacists have direct access to prescription refill information and regularly interact with their patients. Therefore, they are in a unique position to promote optimal medication use. Objectives: To describe how community pharmacists in Quebec, Canada, identify nonadherent patients, monitor medication use and promote optimal medication adherence. Methods: An invitation to complete a web-based survey was published online through different platforms, including a Facebook pharmacists’ group, an electronic newsletter, a pharmacy network forum and e-mail. The survey included questions on participant characteristics, methods used by pharmacists to identify nonadherent patients and monitor medication use and interventions they used to promote medication adherence. Results: In total, 342 community pharmacists completed the survey. The participants were mainly women (71.6%), staff pharmacists (56.7%) and aged 30 to 39 years (34.2%). The most common method to identify nonadherent patients was to check gaps between prescription refills (98.8%). The most common intervention to promote adherence was patient counselling (82.5%). The most common barriers to identifying nonadherent patients were lack of time (73.1%) and lack of prescription information (65.8%), whereas the most common barriers to intervening were anticipation of a negative reaction from their patients (91.2%) and lack of time (64%). Conclusion: Lack of time and lack of prescription information are frequent challenges encountered by community pharmacists regarding effective monitoring and management of patients with poor medication adherence. Pharmacists could benefit from electronic tools based on prescription refills that would provide quick and easily interpretable information on their patients’ medication adherence. Can Pharm J (Ott) 2020;153:xx-xx.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".