J Korean Acad Nurs.  2014 Feb;44(1):97-107. 10.4040/jkan.2014.44.1.97.

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

• Kim HK
• Jeong SH
• Kang HC

• KMID: 1730910
• DOI: http://doi.org/10.4040/jkan.2014.44.1.97

Abstract

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.

Keyword

Stroke; Health knowledge; Decision trees

MeSH Terms

Adult
Aged
Cross-Sectional Studies
Decision Trees
Female
*Health Knowledge, Attitudes, Practice
Humans
Interviews as Topic
Male
Middle Aged
Questionnaires
Risk Factors
Stroke/*pathology

Figure

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  Figure 1 Decision-tree model to identify the subgroups with lower levels of knowledge of risk factors for stroke.

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  Figure 2 Decision-tree model to identify the subgroups with lower levels of knowledge of warning signs for stroke.

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  Figure 3 Decision-tree model to identify the subgroups with lower levels of knowledge of first action for stroke.

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