Identification of the knowledge structure of cancer survivors’ return to work and quality of life: A text network analysis

Abstract

This study aimed to understand the trends in research on the quality of life of returning to work (RTW) cancer survivors using text network analysis. Titles and abstracts of each article were examined to extract terms, including “cancer survivors,” “return to work,” and “quality of life,” which were found in 219 articles published between 1990 and June 2020. Python and Gephi software were used to analyze the data and visualize the networks. Keyword ranking was based on the frequency, degree centrality, and betweenness centrality. The keywords commonly ranked at the top included “breast,” “patients,” “rehabilitation,” “intervention,” “treatment,” and “employment.” Clustering results by grouping nodes with high relevance in the network led to four clusters: “participants and method,” “type of research and variables,” “RTW and education in adolescent and young adult cancer survivors,” and “rehabilitation program.” This study provided a visualized overview of the research on cancer survivors’ RTW and quality of life. These findings contribute to the understanding of the flow of the knowledge structure of the existing research and suggest directions for future research.