Objective To explore the knowledge distribution, knowledge clustering, and the trend in development of wound therapy, by revealing the same keywords with multiple statistical method and social network analysis. Methods We searched the CNKI under the term " wound” , " therapy” , and " wound therapy” in February 2016. After the core keywords had been identified by Bicomb and Endnote X6 software in each stage, the co-occurrence matrix was built. Transformation, dimensionality reduction and clustering of the co-occurrence matrix were finished by SPSS 22.0 software, leading the strategic plot to be built. The visualized network images were drawn using Ucinet 6.0 software. Results The visualized domain knowledge-mapping was successfully built, and it directly reflected the structure of knowledge-mapping of the discipline, as well as key clusters. Boost development had been identified in this research. The subject developed own core research areas and clusters, but there was still lack of fitting characteristics. The newly wound therapeutic techniques had limited correlation with other clusters, while provided limited contributions to forward this subject. However, enriched core keywords had been demonstrated, and formed clear domain parts of this subject. Conclusions The analysis demonstrates that wound therapy has developed well, and hot research points follow the direction of medication treatment. The network of wound therapeutic subject has become mature and completed within a short period. Comprehensive therapy and long term follow-up results according to evidence-based nursing have become the domain field. Moreover, the newly therapeutic techniques should be paid more attention to shift the development of this subject. And the interactive research within this subject and among other regions should be enhanced.
Objective To understand the research status of social network analysis methods in the medicine and health field, help medical scientific research managers quickly understand the publication situation and research hotspots of the methods, and provide references for them to use social network analysis methods to enter deeper research. Methods PubMed, Web of Science, Springer Link, ScienceDirect, China National Knowledge Infrastructure, Wanfang and VIP databases were searched for related literature on social network analysis methods in the medical and health field from the establishment of databases to April 2022. Bibliometric analysis was used to analyze the included articles. Results A total of 432 articles were included, with 424 in Chinese and 8 in English. The included articles were published between 1993 and 2020, involving 154 journals and 913 key words. The number of documents increased rapidly at first, and then entered a stable stage. The hot research directions were the spread and prevention of diseases and the power of social support networks. Conclusions Although the number of applications of social network analysis methods in the medical and health field has increased year by year and the application flexibility has increased, the application depth is still lacking. Scientific researchers should dig deep into the research direction, combine theory with practice, and focus on innovation.
ObjectiveTo provide a scoping review of the healthcare provider patient-sharing network. MethodsPubMed, EMbase, Scopus, ProQuest, Web of Science Core Collection, ScienceDirect, SAGE, Wiley Online Library, Google Scholar, CNKI and WanFang Data databases were electronically searched to collect studies on patient-sharing network of healthcare providers from inception to July 31, 2021. Two reviewers independently screened literature, extracted data and then Arksey and O 'Malley's scoping review method was used to analyze the study. ResultsA total of 110 studies were included. In which, 70.0% were published in 2016 and later, 78.2% were carried out in the United States, 96.4% used secondary data, and 45.5% adopted social network analysis methods such as exponential random graph model. In terms of network characteristics, 43.6% of the studies adopted the theoretical framework of social network theory, and the network node type was mainly 1-mode, accounting for 87.3%. When constructing the physician patient-sharing networks, 64.5% of the studies had a threshold of 1 patient. We also synthesized existing studies on patient-sharing networks of healthcare providers in the light of factors of networks and related outcomes. ConclusionThe studies of healthcare provider patient-sharing network have potentials to improve clinical practice and health policies. Further studies should consider adopting longitudinal design to validate evidence of study, expanding the scope of study subjects except physicians and enriching the evidence of the relationship between network and health-related outcomes.