The Impact of County Medical Community Reform on the Medical Service Efficiency of County-Level Public General Hospitals in China: A Case Study of Shanxi Province
Bibliographic record
Abstract
China introduced the county medical community (CMC) reform, aimed to provide high-quality medical resources to rural citizens, in 2017. This study examines the impact of the reform on the medical service efficiency of county-level public general hospitals in Shanxi Province, China. In total, 92 county-level public general hospitals from Shanxi Province were taken as the research objective, and the super-efficiency SBM-DEA model was applied to measure medical service efficiency. Further, a two-way fixed-effect model was used to evaluate the impact of CMC reform on the medical service efficiency of county-level public general hospitals by using health statistics data from 2014 to 2018. The study reveals that the CMC reform improved the medical service efficiency of county-level public general hospitals by 15.6%. Moreover, the CMC reform had regional heterogeneity in its impact on the medical service efficiency of county-level public general hospitals. The CMC reform improved the medical service efficiency of hospitals in the southern region more than in the northern region of the province. The medical service efficiency of hospitals in the central region was also improved by CMC reform, but the causal relationship was not found significant. Further, hospital-level factors (e.g., fixed assets, hospital stay, and regional health center) and environmental factors (e.g., GDP, population, urbanization rate, and government subsidies) affected the medical service efficiency of county-level public hospitals during the process of promoting the CMC reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".