Performance evaluation of healthcare services system in China considering unbalanced regional development: a meta-frontier approach
Bibliographic record
Abstract
As the foundation of public health protection, the healthcare service system requires rigorous performance assessment to guide reforms and ensure high-quality service delivery. This study develops a novel meta-frontier super directional distance function (DDF) model to evaluate healthcare service performance across 30 Chinese provinces from 2021 to 2023, accounting for regional heterogeneity. The novel meta-frontier super DDF model effectively handles regional heterogeneity and overcomes both the issue of infeasible targets and weak group-level discrimination, thus providing meaningful information (such as meta-efficiency, group efficiency, and technology gap ratio) to guide the development of effective policies to improve efficiency. Results indicate that the traditional meta-frontier model underestimates both meta-efficiency and technology gaps compared to the proposed approach. Key findings reveal that Hainan is the most efficient province in the eastern region, Henan excels in the central region, and Sichuan stands out for its high-quality healthcare services in the west. Based on these insights, the study suggests targeted strategies for less efficient provinces, emphasizing the improvement of management practices and the reduction of regional technology gaps.
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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 | 0.028 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".