Separating Prescription From Dispensation Medicines: Economic Effect Estimation in Japan
Bibliographic record
Abstract
This study examined the effects of the separation between dispensing and prescribing medicines by pharmacists in pharmacies and doctors in medical institutions, respectively (separation system). The methodology avails public national data. The participation of Japanese medical institutions to the separation system was optional according to the legal system. Consequently, its spreading rate for each administrative district is highly variable, allowing us to study its correlation with various medication costs and other factors, such as the generic medicine replacement ratio, proportion of elderly, and number of doctors per 100 000 individuals as independent variables. These four factors are known to be influential in medical compensation. We used regression analysis by the weighted least square method, with dependent variables being costs of daily medicines, specifically, total, internal, one-shot, external, and injection medicines; medical devices, brand-name medicines, generic medicines, and number of prescribed medicines; as well as technical fees. The analysis focused on whether the extent of the separation system reduces costs such as those for medicines, medical devices, technical fees, or number of prescribed medicines. The partial regression coefficient of the spreading rate of the separation system was found to have negative relationships with all daily costs and number of prescribed medicines, as well as the technical fee, except for external medicines, for which most of the market is represented by light analgesics (taken by patients as needed). The necessity of separating prescribing and dispensing is thus low because of the low information asymmetry between doctors and patients. The results revealed that promoting the separation system reduced medication costs, because it normalized the medication market for daily necessities by compensating information asymmetry. Furthermore, the separation system reduced excesses in prescribing medicines.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".