Bibliographic record
Abstract
In the healthcare industry, to reduce the waiting time for patients, policy makers may allow private hospitals (or called the toll system) to enter the market. However, when the total healthcare capacity in the market is limited (e.g. the number of medical staff or equipment is limited), the entrance of the toll system may offer higher salaries to attract medical staff from public hospitals (or called the free system) and reduce its capacity. Then whether or not introducing toll system in the system can reduce the waiting time becomes an issue. In this paper, we investigate the impact of capacity constraint on a two-tier healthcare system. The results show that when the total capacity is tight enough, the two-tier healthcare system often yields less social welfare than the one-tier free system; and when the total capacity is sufficient (the demand does not exceed the total capacity), the two-tier healthcare system improves the social welfare. Specially, we find under certain conditions the capacity constraint can improve social welfare. In addition, if the capacity constraint has a negative effect on the two-tier system’s performance, the government can set an appropriate upper limit for the toll system’s capacity to remove the negative effect.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".