Development of foreign invested hospitals in China: obstacles and coping strategies
Bibliographic record
Abstract
Since the Chinese government allowed foreign investors to open hospitals in China, the development of foreign invested hospitals has been slow. This paper reviews China’s policies and regulations on foreign invested hospitals. The purpose is to identify obstacles hindering the development of foreign invested hospitals and to propose strategies to overcome these obstacles. A case study was conducted to collect data from four foreign invested hospitals in China. The primary data include interviews, field surveys, and site visits. The secondary data include articles from newspapers and websites, hospital documentations, and media reports. The case study revealed four major obstacles facing foreign invested hospitals: unfavorable tax regulations, high service fees, low power status, and difficulty in physician recruitment. To overcome these obstacles, this paper recommends that foreign invested hospitals should develop external relationships with the government and other China’s public hospitals, reduce misunderstanding from patients and physicians, and select location and size wisely. This paper should be valuable for foreign investors who are interested in opening hospitals in China by helping them understand the regulatory context, avoid pitfalls, and develop suitable strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".