Chinese Version of Misinformation Susceptibility Scale Validated for Nurses in Landmark Psychometric Study

False health claims travel across social media faster than any correction, and the nurses who stand at the front line of patient care are increasingly being asked to sort fact from fiction in real time. A research team led by Jiahao Shi and Yue Pan of The First Affiliated Hospital of Jinzhou Medical University has now taken a decisive step toward measuring that vulnerability. In a study published in BMC Nursing on 16 October 2026, the investigators translated the Online Misinformation Susceptibility Scale into Chinese and rigorously tested its psychometric properties among 585 clinical nurses, delivering the first validated Chinese-language instrument of its kind for the nursing workforce.

The stakes of this work could hardly be higher. Online misinformation has emerged as a significant threat to public health, healthcare services, and patient treatment outcomes, and nurses occupy a unique position in the battle against it. They are among the most trusted voices in medicine, frequently consulted by family members and patients about claims circulating online, yet no standardized tool existed to evaluate how susceptible Chinese nurses are to those claims. The new study fills that gap by adapting the Online Misinformation Susceptibility Scale, known as OMISS, for use in China and confirming that it performs reliably in this population.

To carry out the translation, the team followed Brislin’s classic guidelines for cross-cultural adaptation, a widely respected framework in nursing research. The process involved forward translation by bilingual experts, synthesis of the translated versions, backward translation to check for conceptual drift, and review by a committee to ensure that every item preserved its original meaning while reading naturally in Chinese. Content validity was then evaluated by an expert panel, and a pilot test with a small group of clinical nurses confirmed the feasibility of the translated version before the full-scale investigation began.

Sample size was estimated using a formula designed for confirmatory factor analysis, and the researchers ultimately recruited 585 clinical nurses through convenience sampling. Confirmatory factor analysis was selected as the primary statistical approach because the original scale’s factor structure had already been established, allowing the team to test whether the same structure held in the Chinese context. The analysis produced striking results: the one-factor model fit the data exceptionally well, with a comparative fit index of 0.989, a Tucker-Lewis index of 0.982, and a standardized root mean square residual of just 0.043. All fit indices met or exceeded conventional thresholds, providing strong evidence that the Chinese OMISS retains the same underlying structure as the original instrument.

Reliability testing delivered equally impressive numbers. Cronbach’s alpha coefficient for the Chinese version reached 0.963, far above the 0.70 benchmark commonly used in psychometric research, indicating outstanding internal consistency across all items. The scale also demonstrated excellent test-retest reliability, with an intraclass correlation coefficient of 0.912 over a two-week interval, confirming that the instrument produces stable results over time. Together, these findings establish that the Chinese OMISS is both a faithful and a dependable measure of misinformation susceptibility in the nursing population.

The implications extend well beyond the borders of China. The validated scale now offers researchers a standardized tool to assess how vulnerable nurses are to online misinformation, opening the door to studies examining the personal, professional, and organizational factors that shape that vulnerability. It also provides a foundation for designing targeted interventions, from media literacy training to critical appraisal workshops, that could strengthen nurses’ resistance to false claims. Because nurses are consistently rated among the most trusted professionals in surveys of public confidence, equipping them with validated instruments to understand and counter misinformation carries enormous potential for protecting patient care.

The study does come with limitations that the authors themselves acknowledge. The sample was drawn through convenience sampling from a limited number of hospitals, which may restrict the generalizability of the findings to other regions and practice settings. The cross-sectional design also prevents any conclusions about how misinformation susceptibility changes over time or how it might respond to interventions. Future research, the team suggests, should employ more diverse sampling strategies and longitudinal designs to build on this foundation and explore the broader organizational and cultural factors that influence nurses’ engagement with online health information.

Nevertheless, the arrival of a validated Chinese OMISS marks a significant milestone in the effort to understand and combat health misinformation within the nursing workforce. As false claims continue to circulate across digital platforms and shape public attitudes toward medicine, tools like this scale give researchers and educators a way to measure the problem precisely rather than anecdotally. The work by Shi, Pan, and their colleagues provides a rigorous, evidence-based starting point for protecting the integrity of health information in China and offers a template that other countries can adapt for their own nursing populations.

Subject of Research: Psychometric validation of a Chinese-language misinformation susceptibility scale for nurses

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

Article References: Shi, J., Lin, Y., Li, C., Gong, Y., Qin, X., Li, H., & Pan, Y. (2026). Psychometric evaluation of the online misinformation susceptibility scale: a translation and validation study among Chinese nurses. BMC Nursing. https://doi.org/10.1186/s12912-026-05539-2

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05539-2

Keywords: misinformation, nurses, psychometrics, scale validation, China, BMC Nursing, Psychometric, evaluation, online, susceptibility, scale, translation