Psychometric evaluation of the online misinformation susceptibility scale: a translation and validation study among Chinese nurses

With the continued development of the internet, the spread of false and misleading information has become increasingly easy. In the healthcare field, misinformation poses a significant threat to public health, healthcare services, and patient treatment outcomes. Therefore, understanding nurses’ susceptibility to online misinformation is crucial. However, there is a lack of assessment tools for evaluating nurses’ susceptibility to online misinformation in China. To translate the Online Misinformation Susceptibility Scale into Chinese and evaluate its psychometric properties among Chinese nurses. Using Kendall’s method for sample size estimation, this study recruited a total of 585 clinical nurses through convenience sampling. The original scale was translated in accordance with Brislin’s guidelines, and its content validity and the feasibility of the translated version were evaluated through an expert-panel content-validity assessment. Structural validity was assessed using confirmatory factor analysis. Reliability was assessed in terms of internal consistency using Cronbach’s α coefficient. The Chinese version of the OMISS demonstrated acceptable psychometric properties. Confirmatory factor analysis supported a one-factor structure with a good model fit (χ²/df = 4.083, RMSEA = 0.073, 90% CI [0.058, 0.088], CFI = 0.989, TLI = 0.982, GFI = 0.967, and AGFI = 0.935). The overall Cronbach’s α coefficient was 0.963. This study provides preliminary validation of the Chinese version of the OMISS among nurses, offering a potentially useful tool for assessing susceptibility to online misinformation.

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Publication Details

Journal
BMC Nursing
Published
2026-10-16
DOI
https://doi.org/10.1186/s12912-026-05539-2
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
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article

Psychometric evaluation of the online misinformation susceptibility scale: a translation and validation study among Chinese nurses

Yinghui Gong, Chuang Li, Youbei Lin, LI Hongyu et al.
BMC Nursing
Misinformation and Its Impacts
article

Psychometric evaluation of the online misinformation susceptibility scale: a translation and validation study among Chinese nurses

Yinghui Gong, Chuang Li, Youbei Lin, LI Hongyu, Yue Pan, Jiahao Shi, Xiaojing Qin
article en

Abstract

With the continued development of the internet, the spread of false and misleading information has become increasingly easy. In the healthcare field, misinformation poses a significant threat to public health, healthcare services, and patient treatment outcomes. Therefore, understanding nurses’ susceptibility to online misinformation is crucial. However, there is a lack of assessment tools for evaluating nurses’ susceptibility to online misinformation in China. To translate the Online Misinformation Susceptibility Scale into Chinese and evaluate its psychometric properties among Chinese nurses. Using Kendall’s method for sample size estimation, this study recruited a total of 585 clinical nurses through convenience sampling. The original scale was translated in accordance with Brislin’s guidelines, and its content validity and the feasibility of the translated version were evaluated through an expert-panel content-validity assessment. Structural validity was assessed using confirmatory factor analysis. Reliability was assessed in terms of internal consistency using Cronbach’s α coefficient. The Chinese version of the OMISS demonstrated acceptable psychometric properties. Confirmatory factor analysis supported a one-factor structure with a good model fit (χ²/df = 4.083, RMSEA = 0.073, 90% CI [0.058, 0.088], CFI = 0.989, TLI = 0.982, GFI = 0.967, and AGFI = 0.935). The overall Cronbach’s α coefficient was 0.963. This study provides preliminary validation of the Chinese version of the OMISS among nurses, offering a potentially useful tool for assessing susceptibility to online misinformation.

BMC Nursing
Shenyang The Fourth Hospital of People (CN), Affiliated Hospital of Jining Medical University (CN), Jinzhou Medical University (CN)
Good health and well-being
Openalex Percentile: Top 6%
Misinformation and Its Impacts
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