Pharmacist Web-Based Training Program on Medication Use in Chronic Kidney Disease Patients: Impact on Knowledge, Skills, and Satisfaction
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) patients are multimorbid elderly at high risk of drug-related problems. A Web-based training program was developed based on a list of significant drug-related problems in CKD patients requiring a pharmaceutical intervention. The objectives were to evaluate the impact of the program on community pharmacists' knowledge and skills and their satisfaction with the training. METHODS: Pharmacists were randomized to the training program or the control group. Training comprised a 60-minute Web-based interactive session supported by a clinical guide. Pharmacists completed a questionnaire on knowledge (10 multiple-choice questions) and skills (2 clinical vignettes) at baseline and a second time within 1 month. Trained pharmacists completed a written satisfaction questionnaire. Semidirected telephone interviews were conducted with 8 trained pharmacists. Changes in knowledge and skills scores were compared between the groups. RESULTS: Seventy pharmacists (training: 52; control: 18) were recruited; the majority were women with <15 years' experience. Compared with the control group, an adjusted incremental increase in the knowledge score (22%; 95% confidence interval [CI]: 16%-27%) and skills score (24%; 95% CI: 16%-33%) was observed in the training group. Most pharmacists (87%-100%) rated each aspect of the program "excellent'' or "very good." Additional training and adding a discussion forum were suggested to complement the program. DISCUSSION: Pharmacists like the Web-based continuing education program. Over a short time span, the program improved their knowledge and skills. Its impact on their clinical practices and quality of medication use in CKD patients remains to be assessed.
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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) | 0.000 | 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) | 0.005 | 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".