Trends in Nursing Research on Infections: Semantic Network Analysis and Topic Modeling

A text mining and network analysis of topics and trends in major nursing research journals

AIM

This study is set to determine the main topics of the nursing field and to show the changing perspectives over time by analysing the abstracts of several major nursing research journals using text mining methodology.

DESIGN

Text mining and network analysis.

METHODS

Text analysis combines automatic and manual operations to identify patterns in unstructured data. Detailed searches covering 1998-2021 were conducted in PubMed archives to collect articles from six nursing journals: Journal of Advanced Nursing, International Journal of Nursing Studies, Western Journal of Nursing Research, Nursing Research, Journal of Nursing Scholarship and Research in Nursing and Health. This study uses a four-phase text mining and network approach, gathering text data and cleaning, preprocessing, text analysis and advanced analyses. Analyses and data visualization were performed using Endnote, JMP, Microsoft Excel, Tableau and VOSviewer versions. From six journals, 17,581 references in PubMed were combined into one EndNote file. Due to missing abstract information, 2496 references were excluded from the study. The remaining references (n = 15,085) were used for the text mining analyses.

RESULTS

Eighteen subjects were determined into two main groups; research method topics and nursing research topics. The most striking topics are qualitative research, concept analysis, advanced practice in the downtrend, and literature search, statistical analysis, randomized control trials, quantitative research, nurse practice environment, risk assessment and nursing science. According to the network analysis results, nursing satisfaction and burnout and nursing practice environment are highly correlated and represent 10% of the total corpus. This study contributes in various ways to the field of nursing research enhanced by text mining. The study findings shed light on researchers becoming more aware of the latest research status, sub-fields and trends over the years, identifying gaps and planning future research agendas. No patient or public contribution.

Text Network Analysis of Research Topics and Trends on Simulations Using Virtual Patients in Nursing Education

Simulations using virtual patients have been utilized as an effective method in nursing education. However, keyword network analyses and topic modeling on simulations using virtual patients in nursing education have not yet been performed. In this study, 213 articles were retrieved from online research article databases. Abstracts from these articles were extracted, and network analysis was conducted using NetMiner version 4.3 (Cyram Inc, Seongnam, South Korea). Based on the study's analysis, scenario, communication, system, assessment, person, disaster, and management were identified as the keywords with high centrality values. Therefore, they were determined to be influential in the network. After topic modeling, 10 topics were derived as dementia care competency, pain assessment, airway placement management, operating procedure, presence and satisfaction, communication and attitude improvement, platform world, disaster response, game and video usability, and system for confidence. The identified trends in this study will help grasp the trends and insight to guide future research directions on simulations using virtual patients in nursing education.