Health and medical informatics research: Identifying international collaboration patterns at the country and institution level
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».