Do hospitals need to establish multiple hospital districts? A hospital-based perspective on the benefits of scale
Bibliographic record
Abstract
Background: During the fight against COVID-19, China's public hospitals played the main role in taking on the most urgent, dangerous and arduous medical treatment and work. Therefore, in order to promote the high-quality development of hospitals, it is necessary to support some potential public hospitals to build and develop a "One Hospital with Multiple Campuses System" (OHMC) based on controlling the size of single hospitals, and to quickly convert their functions in the event of a severe epidemic. Methods: The Cobb-Douglas production function and log-transformed production function were used to measure the appropriate hospital size for 22 public hospitals in a region of China. Results: The eight OHMC hospitals that planned to be build are basically qualified to handle the conditions and potential of multi-districts from the perspective of economy of scale. The OHMC hospitals in operation appear to have weakened incremental scale rewards, because they are in the process of development, but they are still higher than the overall level of single-campus hospitals. Conclusion: The expansion of hospital scale may bring the advantages of group development, but it may also bring about problems including rising hospital cost, increasing management and operation cost, inefficient allocation of medical resources and unbalanced development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".