Uniform pricing and subsidy coordination mechanism in a two‐tier healthcare system under a co‐payment policy
Bibliographic record
Abstract
Abstract Recently, the lengthy waiting time in public hospitals (called the public system) under the free healthcare policy has become a serious problem. To address this issue, motivated by the Japanese healthcare system, this paper investigates a two‐tier co‐payment healthcare system under a uniform pricing and subsidy coordination mechanism. In such a setting, the public system and the private system (i.e., the private hospitals) compete for market share with different objectives, whereas the government uniformly sets the service price and the subsidy rate to maximize social welfare under a total budget constraint. Compared with two free healthcare policy cases implemented in the Canadian and Australian healthcare systems respectively in terms of social welfare, the results show that when the market demand (or the patient service quality sensitivity) is sufficiently high (sufficiently low), the uniform pricing and subsidy coordination mechanism is better and worse otherwise; and when the patient's waiting sensitivity (or the total government budget) is in an appropriate middle range (sufficiently low or high), the mechanism can outperform than the free policy cases.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".