Optimal pricing and budget decisions in public health systems with delay sensitive patients
Bibliographic record
Abstract
Abstract The congestion of public hospitals for elective treatment in some countries and regions, such as Canada and Hong Kong where the free health policy is implemented, is a serious issue. The main reason is the excessive demand generated by the provision of free service. In response, the government can set appropriate service price and budget for public hospitals to moderate such demand. This is often referred to as the charging policy, implemented in countries such as China. A Stackelberg game is established for a health system consisting of a government, a public health provider and delay sensitive patients. The results show that when the customers' waiting cost is low (e.g., the market demand, the patients delay sensitivity, or the unit capacity cost is low), the free health policy outperforms the charging policy; otherwise, the charging policy is better. Moreover, we find that the equilibrium waiting time and the equilibrium price decrease with the market demand when the funder attaches more importance to patients’ welfare than the budget surplus and the total budget is sufficient.
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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 | 0.019 |
| 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 | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".