J Korean Acad Nurs.  2016 Feb;46(1):50-58. 10.4040/jkan.2016.46.1.50.

A Study on the Knowledge Structure of Cancer Survivors based on Social Network Analysis

• Kwon SY
• Bae KR

• KMID: 2373162
• DOI: http://doi.org/10.4040/jkan.2016.46.1.50

Abstract

PURPOSE
The purpose of this study was to identify the knowledge structure of cancer survivors.
METHODS
For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords.
RESULTS
According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'.
CONCLUSION
Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

Keyword

Neoplasms; Survivors; Knowledge; Social network analysis

MeSH Terms

Cluster Analysis
Databases, Factual
Humans
*Models, Nursing
Neoplasms/pathology/*psychology
Palliative Care
Qualitative Research
Quality of Life
Social Support
Survivors/psychology

Figure

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  Figure 1 Change in the number of researches for cancer survivors by year.

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  Figure 2 The PFnet(q=n-1, r=∞) visualization of the 365 node co-occurrence network.

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