Can health care financing policy be emulated? The Singaporean medical savings accounts model and its Shanghai replica
Bibliographic record
Abstract
BACKGROUND: Each nation's government is searching for a cost-effective health care system. Some nations are developing their health care financing methods through gradual evolution of the existing ones, and others are trying to adopt other nations' successful schemes as their own financing strategies. RESULTS: The Singaporean government seems able to finance its nation's health care with a very low gross domestic product (GDP) input. Since the implementation of the medical savings accounts schemes (MSAs) in 1984, Singaporean government's share of the nation's total health care expenditure dropped from about 50% to 20%. Inspired by Singapore's success, the Chinese government adopted the Singaporean MSAs model as its health care financing schemes for urban areas. Shanghai was the first large urban centre to implement the MSAs in China. Through the study of the Singapore and Shanghai experiences, this article examines whether it is rational to borrow another nation's health care financing model, especially when the two societies have very different socioeconomic characteristics. CONCLUSION: However, the MSAs' success in Singapore did not guarantee its Shanghai success, because health care systems do not work alone. Through study of the MSAs' experiences in Singapore and Shanghai, this paper examines whether it is rational to borrow another nation's health care financing model, especially when the two societies have very different socioeconomic characteristics.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".