Configuration Analysis of Influencing Factors of Technical Efficiency Based on DEA and fsQCA: Evidence from China’s Medical and Health Institutions
Bibliographic record
Abstract
Zhiguang Li,1 Wanying Zhang,1 Aijie Kong,1 Zhiyuan Ding,1 Hua Wei,1 Yige Guo2 1School of Economics and Management, Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China; 2King’s Business School, King’s College London, London, UKCorrespondence: Hua WeiSchool of Economics and Management, Anhui University of Chinese Medicine, 350 Longzihu Road, Xinzhan District, Hefei 230012, People’s Republic of ChinaTel +86-551-68129224Email weihua@ahtcm.edu.cnPurpose: This paper aims to measure the technical efficiency of China’s medical and health institutions from 2012 to 2017 and outline the path to achieve high-quality development.Methods: The DEA-Malmquist was used to evaluate the total factor productivity of medical and health institutions in 31 provinces. A fuzzy set Qualitative Comparative Analysis (fsQCA) was used for configuration analysis of determinants affecting technical efficiency.Results: The average total factor productivity (TFP) of those institutions was 0.965, namely TFP declined averagely by 3.5% annually. The efficiency change and the technical change were 0.998 and 0.967, respectively. The realization paths of high technical efficiency are composed of high fatality rate and high financial allocation-led, high population density and high GDP-led. Low dependency ratio and low financial allocation-led, low fatality rate and low financial allocation-led are the main reasons for low technical efficiency.Conclusion: Due to advanced medical technology and economic development, major cities like Beijing, Shanghai, and Guangdong have attracted a large number of high-level health personnel, achieving long-term and stable health business growth. Hubei, Anhui, and Sichuan also have made rapid development of health care through appropriate financial subsidies and policy supports. The technical changes in Qinghai, Yunnan, and Inner Mongolia are higher than the national average, but the operation and management level of the medical and health institutions is relatively weak. Henan, Jiangxi, and Heilongjiang have a prominent performance in the efficiency change, but the technical change is weaker than the national average.Keywords: China’s medical and health institutions, DEA-malmquist, fsQCA, efficiency, configuration
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".