The effects of increased market competition on hospital services in Shandong and Henan Provinces
Bibliographic record
Abstract
The Chinese government began a major reform of the hospital sector in the early 1980s. The main aim was to increase productivity by phasing out prospective global budgets from the government, and encouraging between-hospital competition for the business of user-pay and insured patients. This goal was to be achieved without unreasonable prejudice to the financial sustainability of hospitals or to the fairness of access and service provision. We explored the effects of these changes by analysing data for four levels of hospital in two of the most populous provinces between 1985 and 1999. We used data envelope analysis, and found that the majority of hospitals experienced a decline in productivity. Social efficiency (measured by the level of provision of unnecessary services) also declined, especially in the largest hospitals that could easily increase the use of expensive technologies. Most hospitals increased their economic sustainability, measured as the ratio between revenue and expenditures. However, the lowest-level hospitals experienced stable or reduced sustainability due to their inability to compete with marketing by higher-level hospitals. We conclude that, although there were many benefits, the overall impact of the introduction of market forces may have been negative. An important factor was that not all aspects (such as supplier-induced demand) were adequately controlled by government agencies. We suggest ways of alleviating the most problematic elements of current arrangements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".