Interventions performed by community pharmacists in one Canadian province: a cross-sectional study
Bibliographic record
Abstract
PURPOSE: Interventions made by pharmacists to resolve issues when filling a prescription ensure the quality, safety, and efficacy of medication therapy for patients. The purpose of this study was to provide a current estimate of the number and types of interventions performed by community pharmacists during processing of prescriptions. This baseline data will provide insight into the factors influencing current practice and areas where pharmacists can redefine and expand their role. PATIENTS AND METHODS: A cross-sectional study of community pharmacist interventions was completed. Participants included third-year pharmacy students and their pharmacist preceptor as a data collection team. The team identified all interventions on prescriptions during the hours worked together over a 7-day consecutive period. Full ethics approval was obtained. RESULTS: Nine student-pharmacist pairs submitted data from nine pharmacies in rural (n = 3) and urban (n = 6) centers. A total of 125 interventions were documented for 106 patients, with a mean intervention rate of 2.8%. The patients were 48% male, were mostly ≥18 years of age (94%), and 86% had either public or private insurance. Over three-quarters of the interventions (77%) were on new prescriptions. The top four types of problems requiring intervention were related to prescription insurance coverage (18%), drug product not available (16%), dosage too low (16%), and missing prescription information (15%). The prescriber was contacted for 69% of the interventions. Seventy-two percent of prescriptions were changed and by the end of the data collection period, 89% of the problems were resolved. CONCLUSION: Community pharmacists are impacting the care of patients by identifying and resolving problems with prescriptions. Many of the issues identified in this study were related to correcting administrative or technical issues, potentially limiting the time pharmacists can spend on patient-focused activities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".