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Original Article
Identification of Subgroups with Lower Level of Stroke Knowledge Using Decision-tree Analysis
Hyun Kyung Kim, Seok Hee Jeong, Hyun Cheol Kang
Journal of Korean Academy of Nursing 2014;44(1):97-107.
DOI: https://doi.org/10.4040/jkan.2014.44.1.97
Published online: February 28, 2014

1College of Nursing, Research Institute of Nursing Science, Chonbuk National University, Jeonju, Korea.

2Department of Informational Statistics, Hoseo University, Asan, Korea.

Address reprint requests to: Jeong, Seok Hee. College of Nursing, Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 561-756, Korea. Tel: +82-63-270-3117, Fax: +82-63-270-3127, awesomeprof@jbnu.ac.kr
• Received: October 31, 2013   • Revised: November 13, 2013   • Accepted: January 27, 2014

© 2014 Korean Society of Nursing Science

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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  • Purpose
    This study was performed to explore levels of stroke knowledge and identify subgroups with lower levels of stroke knowledge among adults in Korea.
  • Methods
    A cross-sectional survey was used and data were collected in 2012. A national sample of 990 Koreans aged 20 to 74 years participated in this study. Knowledge of risk factors, warning signs, and first action for stroke were surveyed using face-to-face interviews. Descriptive statistics and decision tree analysis were performed using SPSS WIN 20.0 and Answer Tree 3.1.
  • Results
    Mean score for stroke risk factor knowledge was 7.7 out of 10. The least recognized risk factor was diabetes and four subgroups with lower levels of knowledge were identified. Score for knowledge of stroke warning signs was 3.6 out of 6. The least recognized warning sign was sudden severe headache and six subgroups with lower levels of knowledge were identified. The first action for stroke was recognized by 65.7 percent of participants and four subgroups with lower levels of knowledge were identified.
  • Conclusion
    Multi-faceted education should be designed to improve stroke knowledge among Korean adults, particularly focusing on subgroups with lower levels of knowledge and less recognition of items in this study.
  • Keywords: Stroke; Health knowledge; Decision trees
Figure 1
Decision-tree model to identify the subgroups with lower levels of knowledge of risk factors for stroke.
jkan-44-97-g001.jpg
Figure 2
Decision-tree model to identify the subgroups with lower levels of knowledge of warning signs for stroke.
jkan-44-97-g002.jpg
Figure 3
Decision-tree model to identify the subgroups with lower levels of knowledge of first action for stroke.
jkan-44-97-g003.jpg
Table 1
Characteristics of Participants (N=990)
jkan-44-97-i001.jpg

*Missing data were not included.

Table 2
Knowledge of Risk Factors, Warning Signs, and First Action for Stroke (N=990)
jkan-44-97-i002.jpg

Figure & Data

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        • Citation for the content below
        Identification of Subgroups with Lower Level of Stroke Knowledge Using Decision-tree Analysis
        J Korean Acad Nurs. 2014;44(1):97-107.   Published online February 28, 2014
        DOI: https://doi.org/10.4040/jkan.2014.44.1.97
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      Identification of Subgroups with Lower Level of Stroke Knowledge Using Decision-tree Analysis
      Image Image Image
      Figure 1 Decision-tree model to identify the subgroups with lower levels of knowledge of risk factors for stroke.
      Figure 2 Decision-tree model to identify the subgroups with lower levels of knowledge of warning signs for stroke.
      Figure 3 Decision-tree model to identify the subgroups with lower levels of knowledge of first action for stroke.

      Figure 1

      Figure 2

      Figure 3

      Identification of Subgroups with Lower Level of Stroke Knowledge Using Decision-tree Analysis

      Characteristics of Participants (N=990)

      *Missing data were not included.

      Knowledge of Risk Factors, Warning Signs, and First Action for Stroke (N=990)
      Table 1 Characteristics of Participants (N=990)

      *Missing data were not included.

      Table 2 Knowledge of Risk Factors, Warning Signs, and First Action for Stroke (N=990)

      Table 1

      Table 2

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