A Scientometric Analysis of Global Health Research
Bibliographic record
Abstract
With the development and deepening of the process of global integration, global health is gaining increasing attention. An increasing number of studies have examined global health from diverse perspectives to promote the realization of global public health. The purpose of this research is to systematically and comprehensively evaluate the knowledge structure, knowledge domain, and evolution trend in the field of global health research. Based on the 14,692 document data retrieved from Web of Science Core Collection from 1996 to 2019, this article carried out a visual analysis of global health research from the perspective of scientific output characteristics, scientific research cooperation networks, keywords, and highly cited literature. The results show that scholars' interest in global health research is increasing, especially after the outbreak of SARS. USA, England, Canada, Australia, and China have the most prominent contributions to global health research. Significant authors, high impact journals and core institutions also identified. The study found that "global health governance", "global health diplomacy", "medical education", "global health education" and "antimicrobial resistance" are the research frontiers and hot spots. This study provides an overview and valuable guidance for researchers and related personnel to find the research direction and practice of global health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.129 | 0.186 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".