Connecting healthcare professionals in Central America through management and leadership development: a social network analysis

Building management and innovation capabilities for global health: a senior executive program

Accurately approaching the major challenges associated with global health management has become a mandatory key point in the training of medical leaders around the world. The Senior Executive Program in Global Health Innovation Management (SEPGHIM) seeks to provide an answer to the need for innovation and managerial capacity building in Global Health and to address the current detachment between Public Health Organizations and Business Schools. In 2019, SEPGHIM's first edition was led by five prestigious academic institutions on three continents. The first cohort included a total of 27 high-level health professionals and executives from 16 countries with 7-10 years of working experience who participated during the 11 months of the course. The program sought to fill an often-found knowledge gap among health professionals in terms of health innovation, leadership, and management. SEPGHIM relied on multiple pedagogical methods conveyed through a robust theoretical and applied syllabus that included case studies, simulations, guest speakers, debates, site visits, and an executive challenge. The program achieved various results. First, it recruited high-level health professionals that ensured diversity of backgrounds, allowing an exchange of experiences and different ways of addressing global health challenges. Second, it created a network of health professionals for possible future collaborations that can anticipate new trends and opportunities in global health and work together with stakeholders from other sectors. This networking was one of the most highly rated benefits by the students. Finally, the participants expressed great eagerness to recommend the program (4.9 out of 5) to other decision-makers and leaders in the global health field. These results provide positive insights regarding the value of such a training program for senior health professionals.

Classification and citation analysis of the 100 top-cited articles on adult spinal deformity since 2011: A bibliometric analysis

BACKGROUND

Studies of the 100 most-cited articles are reported for many subjects. However, none has analyzed the article characteristics associated with high citation frequency. This study aims to (1) graphically depict characteristics of the 100 top-cited articles addressing adult spinal deformity (ASD), (2) diagram the association between articles according to subject and major topic medical subject headings (MeSHs), and (3) investigate whether major topic MeSH correlates with article citation frequency.

METHODS

The 100 top-cited ASD publications since 2011 were retrieved using a PubMed Central search on May 6, 2020. Using titles and abstracts, eight subject categories were identified: surgery, conservative treatment, normal values in spinopelvic alignment, review, cervical alignment, classification, compensatory mechanism, and spine-hip relationship. Sankey diagrams were used to organize the information. Network analysis was performed according to article subject and major topic MeSHs. Pearson's r was used to determine whether the weighted number of citations correlates with major topic MeSHs and the number of citations.

RESULTS

The average number of citations per article was 34.8 (range, 19-156). The most represented country was USA (n = 51). The most productive and highly cited journal was Spine (Phila Pa 1976) (n = 34; average, 38.2 citations per article). The most frequent subject categories and major topic MeSHs were "surgery" (n = 53) and "scoliosis" (weighted count, 9.8), while articles with the subject "compensatory" had the highest average number of citations (64.7). The most highly cited article, by Dr. F. Schwab in 2012, had 156 citations. Network analysis revealed the relationships between these articles according to major topic MeSHs. The weighted number of citations according to major topic MeSHs correlated significantly with article citation frequency (Pearson's r, 0.57; p < 0.001).

CONCLUSION

Multiple characteristics of the 100 top-cited ASD articles are presented in diagrams to guide evidence-based clinical decision-making in ASD.

Predicting article citations using data of 100 top-cited publications in the journal Medicine since 2011: A bibliometric analysis

BACKGROUND

Publications regarding the 100 top-cited articles in a given discipline are common, but studies reporting the association between article topics and their citations are lacking. Whether or not reviews and original articles have a higher impact factor than case reports is a point for verification in this study. In addition, article topics that can be used for predicting citations have not been analyzed. Thus, this study aims to METHODS:: We searched PubMed Central and downloaded 100 top-cited abstracts in the journal Medicine (Baltimore) since 2011. Four article types and 7 topic categories (denoted by MeSH terms) were extracted from abstracts. Contributors to these 100 top-cited articles were analyzed. Social network analysis and Sankey diagram analysis were performed to identify influential article types and topic categories. MeSH terms were applied to predict the number of article citations. We then examined the prediction power with the correlation coefficients between MeSH weights and article citations.

RESULTS

The citation counts for the 100 articles ranged from 24 to 127, with an average of 39.1 citations. The most frequent article types were journal articles (82%) and comparative studies (10%), and the most frequent topics were epidemiology (48%) and blood and immunology (36%). The most productive countries were the United States (24%) and China (23%). The most cited article (PDID = 27258521) with a count of 135 was written by Dr Shang from Shandong Provincial Hospital Affiliated to Shandong University (China) in 2016. MeSH terms were evident in the prediction power of the number of article citations (correlation coefficients  = 0.49, t = 5.62).

CONCLUSION

The breakthrough was made by developing dashboards showing the overall concept of the 100 top-cited articles using the Sankey diagram. MeSH terms can be used for predicting article citations. Analyzing the 100 top-cited articles could help future academic pursuits and applications in other academic disciplines.