Network Analysis in Global Emergency Medicine: Mapping Collaborative Structures and Enhancing Connectivity
Notice bibliographique
Résumé
Introduction Network analysis, which draws from fields including mathematics, sociology, and public health, is essential for interpreting complex data relationships within healthcare information systems. This approach not only highlights intricate care patterns but also enhances the navigability of systems, making complex healthcare information more accessible [1]. Our study builds on previous applications of network analysis in healthcare, specifically examining its use in structuring academic health center websites in the U.S. to improve user-oriented spaces. Our research explores the digital ecosystem of emergency medicine organizations globally, focusing on the International Federation for Emergency Medicine (IFEM). By employing a systematic, automated approach to network mapping, we aim to uncover collaborative patterns and enhance the online presence of emergency resources. This effort supports greater global collaboration and information sharing among emergency medicine organizations, leveraging the strong networks and insights provided by IFEM to ensure universal access to high-quality emergency services. Methodology We utilized a four-step process for mapping and analyzing the global network of emergency medicine organizations: Web Crawling: We collected data from the IFEM website using the Screaming Frog SEO Spider tool. The crawl depth was set to three to ensure comprehensive data capture from IFEM and its connected links. Content Filtering: BeautifulSoup was used to parse HTML content. We filtered the extracted data for relevance to emergency medicine organizations. The data was further refined using the Gemini 1.0 Pro model to isolate pertinent information about the organizations' names and locations. Geolocation Resolution: Location data extracted from the web pages was converted into geographical coordinates using the Geopy library’s Nominatim tool to accurately map of each organization's position. Network Visualization: We constructed a directed network graph using the NetworkX library, with nodes representing organizations and edges representing their interconnections. This graph was visualized with Matplotlib to illustrate the relationships and distribution patterns among the organizations. Results We identified 4,775 external links on the IFEM website and refined them to 156 unique base URLs for in-depth content analysis. Out of 55 countries listed, there were 41 functional links, 10 non-functional, and 4 missing. We effectively isolated 41 relevant multilingual URLs from the functional links, achieving a 100% accuracy rate in identifying pertinent content. Additionally, our analysis uncovered 30 URLs linked to emergency medicine organizations not listed on the IFEM member page, including the American College of Osteopathic Emergency Physicians and the Swiss Society for Emergency and Rescue Medicine. Discussion Our study highlights the utility of network analysis in understanding the digital networks of emergency medicine organizations globally, revealing essential connectivity patterns and their implications for healthcare systems. The methodology used is adaptable for other healthcare areas, enhancing collaboration and addressing language barriers. Findings indicate dynamic changes in digital connectivity, pointing to evolving collaboration and resource sharing. This research provides a foundation for exploring digital connectivity's broader impacts across various sectors. References Niyirora J, Aragones O. Network analysis of medical care services. Health Informatics J. 2020 Sep;26(3):1631-1658. doi: 10.1177/1460458219887047. Epub 2019 Nov 18. PMID: 31735109.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,008 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».