Health and medical informatics research: Identifying international collaboration patterns at the country and institution level
Bibliographic record
Abstract
Objective. In this study, we employed a bibliometric approach to identify and analyze international collaboration trends between countries and institutions engaged in the publication of research on health and medical informatics over the past decade, spanning 2014 to 2023. Design/Methodology/Approach. This study was designed with a particular emphasis on examining scientific productivity and analyzing social networks. We extracted the most relevant literature on the subject from the Scopus database. The data were organized to analyze productivity and citation impact by country and institution. In both cases, countries and institutions were ranked by the total number of papers and citations to identify the most productive and impactful nations and to facilitate a comparison of their performance on a regional and global scale. In the context of network analysis, we identified countries and institutions according to their prestige, influence, and importance. To this end, we employed centrality measures based on the data set representing node connections. Results/Discussion. Scientific productivity in health and medical informatics is concentrated mainly in developed countries. Europe demonstrates a considerable presence, as evidenced by the contributions of countries such as France, Italy, Spain, and Switzerland. However, the leadership of the United States and the United Kingdom is a notable example of the relationship between productivity and citation impact. The United States is identified as the most centralized nation, with 115 direct connections. Other countries of note include the United Kingdom, Germany, Canada, and Switzerland. Regarding influence, Germany is the most prominent country, and in terms of prestige, the United States is once again the leader. The North American region is the most influential and prestigious in the field, while Europe is distinguished by its network structure's incredible diversity and collaboration. The countries that play a pivotal role in this context are Germany, the United Kingdom, France, the Netherlands, and Switzerland. Among the institutions that stand out for their high productivity are Harvard Medical School, the University of Washington, the Mayo Clinic, and the University of Toronto. Harvard Medical School is the most important institution on the map of institutional collaborations. The University of Washington also stands out, along with the Mayo Clinic and Columbia University. Regarding influence, Harvard Medical School and the Mayo Clinic are the most influential institutions. The University of Washington leads in prestige, along with the Vanderbilt University. Conclusions. The analysis of scientific collaboration in health and medical informatics demonstrates that North America and Europe are the preeminent regions, exhibiting dense and well-connected networks that facilitate the global integration of scientific knowledge. Asia, with key countries such as India and the United Arab Emirates, is emerging as an essential region, especially regarding intermediation and prestige. While Latin America and Africa are less represented, there is potential for these regions to increase their participation by expanding their collaborative networks, which is critical to improving the impact and visibility of their research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".