Sun Young Kwon; Ka Ryeong Bae

doi:10.4040/jkan.2016.46.1.50

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Original Article
A Study on the Knowledge Structure of Cancer Survivors based on Social Network Analysis: Sun Young Kwon, Ka Ryeong Bae; Journal of Korean Academy of Nursing 2016;46(1):50-58.
DOI: https://doi.org/10.4040/jkan.2016.46.1.50
Published online: February 29, 2016

¹Knowledge Management Institute, Sungkyunkwan University, Seoul, Korea.

²College of Nursing, Yonsei University, Seoul, Korea.

Address reprint requests to: Bae, Ka Ryeong. College of Nursing, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. Tel: +82-10-6652-8385, Fax: +82-2-392-5440, baekr8385@naver.com

• Received: June 10, 2015 • Revised: June 23, 2015 • Accepted: October 9, 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0/) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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Abstract
NOTES
REFERENCES

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.
Keywords: Neoplasms; Survivors; Knowledge; Social network analysis

The authors declared no conflict of interest.

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Figure 1

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

Figure 2

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

Table 1

Search Queries and Results on Web of Science

Table 2

Research Journals and Articles about 6 Facets and Queries on Web of Science (N=1,099)

Table 3

Frequencies of Keywords in 1099 Cancer Survivors Researches (N=6,543)

rTBC=Triangle betweeness centrality (normalized); Hub/Auth=Hub & authorities; Clu=Cluster.

Figure & Data

REFERENCES

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

J Korean Acad Nurs. 2016;46(1):50-58. Published online February 29, 2016

DOI: https://doi.org/10.4040/jkan.2016.46.1.50

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

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

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

Figure 1

Figure 2

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

Table 1

Search Queries and Results on Web of Science

Table 2

Research Journals and Articles about 6 Facets and Queries on Web of Science (N=1,099)

Table 3

Frequencies of Keywords in 1099 Cancer Survivors Researches (N=6,543)

rTBC=Triangle betweeness centrality (normalized); Hub/Auth=Hub & authorities; Clu=Cluster.

Table 1 Search Queries and Results on Web of Science

Table 2 Research Journals and Articles about 6 Facets and Queries on Web of Science (N=1,099)

Table 3 Frequencies of Keywords in 1099 Cancer Survivors Researches (N=6,543)

rTBC=Triangle betweeness centrality (normalized); Hub/Auth=Hub & authorities; Clu=Cluster.