Physician-pharmacist collaborative care for dyslipidemia patients: Knowledge and skills of community pharmacists
Bibliographic record
Abstract
INTRODUCTION: In a physician-pharmacist collaborative-care (PPCC) intervention, community pharmacists were responsible for initiating lipid-lowering pharmacotherapy and adjusting the medication dosage. They attended a 1-day interactive workshop supported by a treatment protocol and clinical and communication tools. Afterwards, changes in pharmacists' knowledge, their skills, and their satisfaction with the workshop were evaluated. METHODS: In a descriptive study nested in a clinical trial, pharmacists assigned to the PPCC intervention (n = 58) completed a knowledge questionnaire before and after the workshop. Their theoretical skills were evaluated with the use of a vignette approach (n = 58) after the workshop and their practical skills were assessed by direct observation with study patients (n = 28). RESULTS: The mean (SD) overall knowledge score was 45.8% (12.1%) before the workshop; it increased significantly to 89.3% (8.3%) afterwards (mean difference: 43.5%; 95% CI: 40.3%-46.7%). All the pharmacists had an overall theoretical-skill score of at least 80%, the minimum required to apply the PPCC in the trial. From 92.9% to 100% of the pharmacists' interventions with study patients complied with the treatment protocol. DISCUSSION: In primary care, a short continuing-education program based on a specific treatment protocol and clinical tools is necessary and probably sufficient to prepare pharmacists to provide advanced pharmaceutical care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".