Research trends related to childhood and adolescent cancer survivors in South Korea using word co-occurrence network analysis

Abstract

Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI).

Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling.

Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life".

Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

Keywords: Adolescent; Cancer survivors; Child; Research; Social networking.

Figure 1.

The study process. KCI, Korea citation index; LDA, latent Dirichlet allocation.

Figure 2.

Visualization of analysis. (A) Map of degree centrality. (B) Map of eigenvector centrality. (C) Map of community. (D) Word cloud according to the results of topic modeling. BMD, bone mineral density; BMI, body mass index.