Scientometric trends and knowledge maps of global health systems research
Bibliographic record
Abstract
BACKGROUND: In the last few decades, health systems research (HSR) has garnered much attention with a rapid increase in the related literature. This study aims to review and evaluate the global progress in HSR and assess the current quantitative trends. METHODS: Based on data from the Web of Science database, scientometric methods and knowledge visualization techniques were applied to evaluate global scientific production and develop trends of HSR from 1900 to 2012. RESULTS: HSR has increased rapidly over the past 20 years. Currently, there are 28,787 research articles published in 3,674 journals that are listed in 140 Web of Science subject categories. The research in this field has mainly focused on public, environmental and occupational health (6,178, 21.46%), health care sciences and services (5,840, 20.29%), and general and internal medicine (3,783, 13.14%). The top 10 journals had published 2,969 (10.31%) articles and received 5,229 local citations and 40,271 global citations. The top 20 authors together contributed 628 papers, which accounted for a 2.18% share in the cumulative worldwide publications. The most productive author was McKee, from the London School of Hygiene & Tropical Medicine, with 48 articles. In addition, USA and American institutions ranked the first in health system research productivity, with high citation times, followed by the UK and Canada. CONCLUSIONS: HSR is an interdisciplinary area. Organization for Economic Co-operation and Development countries showed they are the leading nations in HSR. Meanwhile, American and Canadian institutions and the World Health Organization play a dominant role in the production, collaboration, and citation of high quality articles. Moreover, health policy and analysis research, health systems and sub-systems research, healthcare and services research, health, epidemiology and economics of communicable and non-communicable diseases, primary care research, health economics and health costs, and pharmacy of hospital have been identified as the mainstream topics in HSR fields. These findings will provide evidence of the current status and trends in HSR all over the world, as well as clues to the impact of this popular topic; thus, helping scientific researchers and policy makers understand the panorama of HSR and predict the dynamic directions of research.
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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 | 0.017 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.128 | 0.154 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".