A Nationwide Survey on Medication Follow-up Care by Community Pharmacists: From The Japanese Nationwide Pharmacy Collaboration Survey in 2023
Bibliographic record
Abstract
In Japan, the Pharmaceutical and Medical Device Act was amended in December 2019, and now requires pharmacists to follow-up on patients during treatment. Although there have been some studies on the effectiveness of follow-ups by pharmacists, there are no reports on the status of implementation in clinical practice. We conducted a nationwide survey on follow-up care to investigate the actual situation. We randomly selected 10% of community pharmacies in each prefecture and conducted a survey. We built a web-based system for the collection of basic information on the pharmacies and follow-up cases. A total of 561 pharmacies were pre-entered. Of these, 110 pharmacies (19.6%) reported 326 follow-up cases. Information was provided to doctors in 129 cases (39.6%), of which prescription proposals were made in 10 (7.8%) instances. The follow-up implementation rate based on the number of prescriptions dispensed was estimated to be 0.84% (95% confidence interval: 0.76-0.94%). This study revealed the status of follow-ups in clinical practice. Pharmacists can contribute to the optimization of drug treatment by providing follow-up information to doctors and making prescription proposals.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".