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Validity and Reliability of the Korean Version of the Climate, Health, and Nursing Tool
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Da Woon Jeong, Gwang Suk Kim, Min Kyung Park
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J Korean Acad Nurs 2022;52(2):173-186. Published online April 30, 2022
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DOI: https://doi.org/10.4040/jkan.21211
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 Abstract
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- Purpose
Climate change has various negative effects on human health, which has resulted in increased burden on the health care system. Nurses contribute significantly to assessing climate-related health risks and creating a healthy environment. This study aimed to evaluate the reliability and validity of the Korean version of the Climate, Health, and Nursing Tool (K-CHANT) to measure nurses’ awareness, motivation, concern, and behaviors at work and at home regarding climate change and health.
Methods
The 22 items of English CHANT were translated into Korean with forward-backward translation techniques. Internal consistency reliability, test-retest reliability, and construct validity using confirmatory factor analysis were performed using SPSS WIN (25.0) and AMOS (26.0). Survey data were collected from 220 master’s, doctoral, and post-doctoral nursing students.
Results
The K-CHANT consists of 20 items across 5 domains.Two items of the original CHANT were excluded because of low content validity index and standardized regression weights. The internal consistency reliability of the K-CHANT, assessed by Cronbach’s αá was .81, with an intraclass correlation coefficient of .66~.90. The five subscales model was validated by confirmatory factor analysis (SRMR < .08, RMSEA < .08, AGFI > .70, CFI > .70).
Conclusion
The K-CHANT has satisfactory construct validity and reliability to measure nurses’ awareness, motivation, concern, and behaviors at work and at home regarding climate change and health. Future research should examine nurses’ perceptions and behaviors related to the health effects of climate change and develop an action plan to improve it.
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Citations
Citations to this article as recorded by  - Nurses' Perceptions and Behaviours Regarding Climate Change and Health: A Quantile Regression Analysis
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