The Changing Role of Government in Financing Health Care: An International Perspective
Bibliographic record
Abstract
This paper explores the changing role of government involvement in health care financing policy outside the United States.It provides a review of the economics literature in this area to understand the implications of recent policy changes on efficiency, costs and quality.Our review reveals that there has been some convergence in policies adopted across countries to improve financing incentives and encourage efficient use of health services.In the case of risk pooling, all countries with competing pools experience similar difficulties with selection and are adopting more sophisticated forms of risk adjustment.In the case of hospital competition, the key drivers of success appear to be what is competed on and measurable rather than whether the system is public or private.In the case of both the success of performance-related pay for providers and issues resulting from wait times, evidence differs both within and across jurisdictions.However, the evidence does suggest that some governments have effectively reduced wait times when they have chosen explicitly to focus on achieving this goal.Many countries are exploring new ways of generating revenues for health care to enable them to cope with significant cost growth.However, there is little evidence to suggest that collection mechanisms alone are effective in managing the cost or quality of care.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".