Community pharmacists’ experiences with the Saskatchewan Medication Assessment Program
Bibliographic record
Abstract
Background:The Saskatchewan Medication Assessment Program (SMAP) is a publicly funded community pharmacy–based medication assessment service with limited previous evaluation. The purpose of this study was to explore community pharmacists’ experiences with the SMAP.Methods:Online, self-administered questionnaire that consisted of a combination of 53 Likert scale and free-text questions. All licensed pharmacists who were practising in a community pharmacy setting in Saskatchewan were eligible to participate.Results:Response rate was 20.3% (n = 228/1124). Most respondents agreed that the SMAP is achieving all of its intended purposes. For example, 89.7% agreed that the SMAP improved medication safety for patients who receive the service. Most pharmacists enjoyed performing the assessments (84.6%) and were confident in their ability to identify drug-related problems (88.3%). Pharmacists reported lack of time, patients having difficulty coming to the pharmacy and restrictive eligibility criteria as the top barriers to the SMAP. Good teamwork, employer support and personal professional commitment were the top recognized facilitators. Respondents made several suggestions to improve the SMAP in the free-text areas of the questionnaire.Conclusions:Community pharmacists in Saskatchewan were positive and confident about performing medication assessments, and most agreed that the SMAP is achieving all of the intended purposes. Respondents also identified several barriers to providing SMAP services, which have resulted in specific recommendations that should be addressed to improve the 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".