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Yun, Ryu, and Lee: Text Network Analysis Related to Disclosure of Cancer Diagnosis among Korea and other Countries
Original Article

Asian Oncol Nurs 2018;18(3):154-162.

Published online: 5 September 2018

DOI: https://doi.org/10.5388/aon.2018.18.3.154

Text Network Analysis Related to Disclosure of Cancer Diagnosis among Korea and other Countries

Jin Hui Yun1, Eunjung Ryu2, So Young Lee3

1Dongjak Branch Office, National Health Insurance Corporation, Seoul, Korea

2Department of Nursing, Chung-Ang University, Seoul, Korea

3Department of Nursing, Graduate School of Chung-Ang University, Seoul, Korea

∗ This article is a revision of the first author's master's thesis from Chung-Ang University.

Address reprint requests to: Lee, So Young Department of Nursing, Graduate School of Chung-Ang University, 84 Heuksuk-ro, Dongjak-gu, Seoul 06974, Korea Tel: +82-2-820-5671 Fax: +82-2-824-7961 E-mail: her2sso@cau.ac.kr

Received 16 Aug 201814 Sep 2018       Accepted 17 Sep 2018

Copyright © 2018 Korean Ophthalmological Society

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/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

초록

Purpose

This study aimed to identify and compare research topics related to disclosure of cancer diagnosis among Korea and other countries using text network analysis.

Methods

Abstracts from 119 studies for the period of 2000~2015 were analyzed. An integrative literature review and text network analysis were applied to examine the research. The keywords from each article's abstracts were extracted by using a program, KrKwic, and analyzed using network-related measures including degree centrality, and cluster-ing using the NetMiner program.

Results

The most important core keywords; ‘patient’, ‘cancer’, ‘diagnosis’, ‘disclosure’, ‘truth’, ‘physician’, ‘family’, ‘telling’, ‘information’, ‘preference’, ‘member’, ‘age’, and ‘tell’ ranked highly. Asian countries as Korea, Japan, and China showed a similar high centrality of degree of connection in family, which appeared as a factor that influences cancer diagnosis disclosure.

Conclusion

These findings showed knowledge structure of disclosure of cancer diagnosis and its research trends. The 11 topics identified in this comparative study can provide further starting points for research of communication with cancer patients and their family.

Keywords: Neoplasms, Disclosure, Knowledge, Social Network Analysis

REFERENCES

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Fig. 1.
Flow diagram of study selection and process to network analysis.
aon-18-154f1.tif
Table 1.
The Degree Centrality of Keywords in Total Studies
Rank Keyword Degree centrality
1 Patient 1.00
2 Cancer 1.00
3 Diagnosis 1.00
4 Disclosure 1.00
5 Truth 1.00
6 Physician 1.00
7 Family 1.00
8 Telling 1.00
9 Information 1.00
10 Preference 1.00
11 Member 1.00
12 Age 1.00
13 Tell 1.00
14 Care 1.00
15 Disclose 1.00
16 Men 1.00
17 Treatment 1.00
18 Health 0.98
19 General 0.98
20 Different 0.98
21 Hospital 0.98
22 Practice 0.98
23 Communication 0.98
24 Questionnaire 0.98
25 Doctor 0.98
26 Attitude 0.98
27 Professional 0.96
28 Disease 0.96
29 Factor 0.96
30 Medical 0.96
31 Issue 0.94
32 Illness 0.94
33 Oncology 0.94
34 Terminal 0.94
35 Experience 0.94
36 Prognosis 0.94
37 Life 0.90
38 Interview 0.90
39 Bad 0.90
40 Difference 0.88
41 Disclosing 0.86
42 Effect 0.84
43 Nurse 0.84
44 Breaking 0.84
45 Advanced 0.82
46 Self 0.82
47 Cultural 0.82
48 Caregiver 0.80
49 Japanese 0.78
50 Stage 0.78
Table 2.
The Top 25 Keywords in Degree Centrality of Disclosure of Cancer Diagnosis among Korea and Other Countries
Rank Korea
 Italy
 Japan
 China
 United States
Keyword DC Keyword DC Keyword DC Keyword DC Keyword DC
1 Patient 0.94 Cancer 0.88 Cancer 0.88 Patient 0.84 Patient 0.76
2 Family 0.92 Patient 0.88 Patient 0.88 Family 0.82 Cancer 0.71
3 Tell 0.90 Diagnosis 0.84 Disclosure 0.86 Disclosure 0.82 Physician 0.65
4 Cancer 0.90 Men 0.78 Diagnosis 0.84 Cancer 0.78 Diagnosis 0.63
5 Truth 0.88 Physician 0.78 Family 0.80 Truth 0.76 Disclosure 0.59
6 Men 0.86 Disclosure 0.78 Men 0.71 Age 0.71 Men 0.55
7 Telling 0.84 Information 0.69 Prognosis 0.69 Attitude 0.71 Information 0.55
8 Care 0.82 Medical 0.67 Physician 0.69 Men 0.69 Practice 0.53
9 Doctor 0.82 Age 0.65 Tell 0.67 Tell 0.69 Age 0.47
10 Diagnosis 0.82 Family 0.63 Member 0.65 Telling 0.69 Prognosis 0.45
11 Medical 0.78 Cultural 0.59 Truth 0.65 Member 0.67 Effect 0.43
12 Treatment 0.73 Doctor 0.57 Age 0.61 Questionnaire 0.67 Questionnaire 0.43
13 Hospital 0.71 Care 0.55 Information 0.61 Information 0.67 Disclosing 0.41
14 Disclosure 0.71 Practice 0.55 Telling 0.61 Diagnosis 0.63 Medical 0.41
15 Disease 0.69 Prognosis 0.55 Advanced 0.55 Disease 0.55 Treatment 0.39
16 Physician 0.69 Attitude 0.55 Communication 0.55 Disclose 0.53 Preference 0.39
17 Experience 0.65 Factor 0.53 Disclose 0.53 Bad 0.53 Family 0.39
18 Questionnaire 0.65 Oncology 0.53 Hospital 0.51 Stage 0.49 Factor 0.37
19 Terminal 0.63 Truth 0.53 Attitude 0.51 Different 0.49 Life 0.33
20 General 0.61 Treatment 0.51 Disclosing 0.49 Doctor 0.49 Communication 0.33
21 Preference 0.61 Hospital 0.51 Care 0.45 Physician 0.49 Bad 0.33
22 Nurse 0.59 Difference 0.49 Japanese 0.43 Care 0.47 Disclose 0.31
23 Illness 0.57 Communication 0.49 Illness 0.43 Oncology 0.47 Oncology 0.29
24 Information 0.57 Disease 0.47 Life 0.41 Terminal 0.47 Experience 0.29
25 Factor 0.55 Questionnaire 0.47 General 0.41 Illness 0.45 Self 0.27

DC= Degree centrality.

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