International publication trends and collaboration performance of China in healthcare science and services research
Bibliographic record
Abstract
BACKGROUND: In recent years, China's healthcare reforms and related studies have drawn particular global attention. The main objective of this study is to evaluate quantitatively the publication trends and collaboration performance of China in healthcare science and services (HSS) research. METHODS: Scientometric methods and visualization technology were used to survey the growth and development trends of HSS research based on the Web of Science publications during the past 15 years. RESULTS: China's international publications on HSS research increased rapidly compared to those of the global HSS and Chinese scientific studies. Growth trends indicate that collaboration among countries, institutions and authors has also increased. China's leading partners were all developed countries, such as the US, the UK, Australia and Canada, which have contributed to the majority of the joint publications. The academic impact of publications involving partners from European and American countries was relatively higher than those involving partners from Asian countries. Prominent institutions were universities that could be primarily classified into two groups, namely, Mainland China on the one hand and Hong Kong universities and foreign universities on the other. The most prominent actors were elite institutions, such as Peking University, Fudan University, Chinese University of Hong Kong, University of Hong Kong. The papers published by the Chinese Ministry of Health had relatively high academic impact, whereas those published by Mainland China universities alone had a lower academic impact compared to foreign cooperation papers. Issues related to the Chinese healthcare reform, priority diseases (e.g., breast cancer, HIV/AIDS, tuberculosis, etc.), health systems performance, quality of life and measurement tools, aging problems and research methods have been the most popular HSS topics in China in recent years. CONCLUSIONS: Despite the extensive achievement of the Chinese HSS reforms and research, gaps and challenges remain to be addressed, including those related to health insurance and the effects of the evaluation of essential medicine systems, human resources training and allocation in the health sector, government hospitals reforms and health services systems remodeling. These findings could help scholars and decision-makers understand the current status and likely future trends of the Chinese HSS research, and help them select the most appropriate collaboration partners and policies.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.089 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".