Evaluation and convergence analysis of the medical service efficiency in rural medical health centers China
Bibliographic record
Abstract
Abstract Background Rural Medical Health Centers (RMHCs) are the foundation of the three-level primary medical and healthcare service in China. The efficiency of RMHCs is related to the rationale behind healthcare resource allocation for China’s 560 million rural population. Methods This study analyzed the dynamic changes in efficiency of RMHCs and its convergence using the non-oriented SBM–DEA window model and convergence model in Shanxi Province, China. Data was obtained from the Shanxi Rural Health Institute’s 2014–2018 Health Statistics Report, involving 12360 RMHCs. Results Findings show that the medical service efficiency delivered in RMHCs is low. The average scores for the comprehensive technical efficiency and pure technical efficiency of the RMHCs in China from 2014 to 2018 were only 0.0568 and 0.0615 respectively, with nearly 68% of the values being lower than their average scores. The comprehensive technical efficiency and pure technical efficiency of RMHCs from 2014–2018 exhibited a downward trend year on year. The convergence analysis results also showed that current rural health clinic medical service efficiency had \({\alpha }\) convergence, absolute convergence and conditional convergence. If appropriate policies are followed, the medical service efficiency of the RMHCs can be improved to reach a steady-state level. Conclusions The medical service efficiency of RMHCs remains low and may gradually decline. The government should promote the efficiency of the medical service in RMHCs by formulating corresponding medical and health resource allocation policies to reach an optimal level.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".