Japanese Community Pharmacists’ Dispensing Influences Medicine Price Reduction more than Prescription Numbers
Bibliographic record
Abstract
This study examined the economic efficiency of the separation of prescription and dispensation medicines between doctors in medical institutions and pharmacists in pharmacies. The separation system in Japanese prefectures was examined with publicly available data (Ministry of Health, Labour and Welfare, 2012-2014; retrieved from http://www.mhlw.go.jp/topics/medias/year). We investigated whether the separation system reduces the number of medicines or the medication cost of a prescription because of separating the economic management between prescribing and dispensing and the effect of mutual observation between doctors and pharmacists. It is optional for Japanese medical institutions to participate in the separation system. Consequently, the spreading rate of the separation system in each administrative district is highly variable. We examined the separation system effect using the National Healthcare Insurance data for three years, 2012-2014. We tested whether the separation system ratio for each prefecture was significantly correlated to the medication price or the number of medicines on a prescription. If spreading the separation system influenced the price of prescribed daily medications or the number of medicines, the correlation would be significant. As a result, the medication price was significantly negatively correlated with the separation system ratio, but the number of medicines was not significant. Therefore, the separation system was effective in reducing daily medication cost but had little influence on reducing the number of daily medicines. This was observed over three years in Japan.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".