Bridging international borders through global health diplomacy: A comprehensive bibliometric analysis of the state of play and leads for advancing this domain
Bibliographic record
Abstract
Background: Global health diplomacy (GHD) is an emerging intersection of health and international relations, particularly in transnational health challenges. Though growingly important, especially in the current global health scenario, this study aimed to perform a bibliometric analysis of GHD to identify emerging themes, leading contributors, research gaps for further studies and policy directions. Methods: A bibliometric analysis was done on SCOPUS, and a return of 242 articles published between 2007 and 2024 contained the keyword "global health diplomacy." The data was analyzed using Biblioshiny and then exported to Microsoft Excel for thematic coding. Key indicators included publication trends, co-authorship networks, and keyword co-occurrences to establish key trends and gaps. Results: A growing body of research observed an annual growth rate of 7.65% [95% CI]. North American and European countries led the research, especially the United States, Canada, and the United Kingdom. The dominant themes included vaccine diplomacy, global health, Artificial Intelligence-Machine Learning and digital health, governance, and international cooperation. However, there were significant gaps, including underrepresentation from low-and middle-income countries (LMICs), limited focus on noncommunicable diseases (NCDs) including mental health, and neglected climate-health intersections. Conclusion: This study highlights the fast growth and changing nature of GHD research while indicating some key gaps that deserve further research. Strengthening contributions of LMICs, expanding thematic focus to NCDs and environmental health, and fostering interdisciplinary approaches are crucial for advancing the field. The findings are highly relevant for policy and research purposes and will push forward an impactful GHD for global health challenges.
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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 | 0.027 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.186 | 0.277 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| 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".