Predictors of online health misinformation susceptibility among nursing students in Greece

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Objective To identify predictors of online health misinformation susceptibility in nursing students. In particular, we examined the impact of several demographic variables and trust in scientists on online health misinformation. Methods A cross-sectional study was conducted in Greece, with data collected through an online survey in October 2025. We collected data on several demographic characteristics such as gender, age, financial status, level of trust in websites, interest in politics, and the amount of time participants spend daily on the internet and social media. To assess nursing students’ susceptibility to online health misinformation, we employed the Health-Related Online Misinformation Susceptibility Scale. Trust in scientists was assessed using the Trust in Scientists Scale. Results Our sample included 388 nursing students with a mean age of 21 years. Among our students, 82.7% exhibited a high level of susceptibility to online health misinformation. Multivariable analysis showed that students with lower trust in scientists had also higher levels of online health misinformation susceptibility. Moreover, lower financial status and lower interest in politics were associated with increased online health misinformation susceptibility. Our analysis showed a positive association between daily time in web/social media and online health misinformation susceptibility. Conclusion A substantial proportion of nursing students demonstrated a high susceptibility to online health misinformation. Factors such as trust in scientists, financial status, interest in politics, and the amount of time spent daily on web and social media platforms were found to influence this susceptibility. health misinformation susceptibility nursing students scientists trust fake news Introduction Unlike disinformation, which is deliberately fabricated to mislead, misinformation often arises from misunderstanding, misinterpretation, or lack of verification before dissemination. It encompasses a wide range of content, including inaccurate news articles, pseudoscientific claims, conspiracy theories, and health-related myths circulating on social media, websites, and other online channels (Altay, Berriche, Heuer, et al., 2023 ; Roozenbeek & Van Der Linden, 2024 ). For example, Altay et al. define misinformation as “false and misleading information”, noting that it includes pseudoscience and conspiracy theories but excludes satire or parody content (Altay, Berriche, & Acerbi, 2023 ). Similarly, Broda and Strömbäck emphasize that misinformation has become increasingly prevalent in the digital era, particularly during major sociopolitical events, contributing to what some scholars call a “misinformation age” (Broda & Strömbäck, 2024 ). In particular, online misinformation refers to false or misleading information that is shared through digital platforms without the intent to deceive, although it can still cause harm by shaping beliefs, attitudes, or behaviors. Misinformation can be understood as a multifaceted phenomenon characterized by three core attributes. First, it involves factual inaccuracy, wherein the information disseminated is objectively false or misleading. This may include fabricated data, distorted interpretations, or erroneous claims that lack empirical support (Lewandowsky et al., 2017 ; Roozenbeek et al., 2023 ). Second, misinformation is often spread unintentionally; individuals who share such content typically believe it to be accurate, which distinguishes it from deliberate disinformation (Perfors, 2025 ). Third, misinformation carries the potential for harm, as it can shape public opinion, influence decision-making processes, and erode trust in institutions (Ecker et al., 2025 ; Roozenbeek et al., 2023 ). Health misinformation refers to false, inaccurate, or misleading information related to health topics, shared without intent to deceive, often by individuals who believe the information to be true (Office of the U.S. Surgeon General, 2021 ). The impact of health misinformation on public health is profound. For instance, during the COVID-19 pandemic, misinformation led to vaccine hesitancy, rejection of public health measures, and adoption of unproven treatments, contributing to preventable illness and death (Lee et al., 2022 ; Pierri et al., 2022 ; Rodrigues et al., 2023 ). For example, one widely circulated false claim suggested that drinking methanol could cure coronavirus, resulting in hundreds of deaths and thousands of hospitalizations globally (Islam et al., 2020 ; Mousavi-Roknabadi et al., 2022 ). Beyond acute crises, misinformation about routine health practices—such as the safety of childhood vaccines—has fueled outbreaks of previously controlled diseases like measles (Adeoye et al., 2025 ; The Lancet Infectious Diseases, 2025 ). In this context, health misinformation erodes trust in medical institutions and professionals, undermines adherence to evidence-based treatments, and exacerbates health inequities. Vulnerable populations, including those with lower health literacy or higher medical mistrust, are disproportionately affected, making misinformation a driver of inequitable health outcomes (Carletto et al., 2025 ; Lubej & Kirbiš, 2025 ). These instances underscore the pervasive and consequential nature of misinformation across domains including health, highlighting the need for robust strategies to detect and mitigate its impact (Broda & Strömbäck, 2024 ; Rau & Premo, 2025 ). Moreover, digital platforms amplify this problem by enabling rapid, large-scale dissemination of misleading content, often through algorithms that prioritize engagement over accuracy. Thus, exposure to inaccurate or misleading health content on digital platforms can create knowledge gaps and foster misconceptions, which may compromise evidence-based practice. However, the body of literature addressing the measurement of health misinformation among nursing students is notably limited. Despite the growing recognition of misinformation as a critical challenge in healthcare education, empirical research quantifying its prevalence or assessing susceptibility within this population remains scarce. To date, only two qualitative studies have explored selected dimensions of health misinformation among nursing students, focusing primarily on perceptions and experiences rather than systematic measurement (Enslein, 2024 ; Yang et al., 2024 ). In particular, Enslein conducted a qualitative study involving 14 nursing students who were recruited to watch and listen to birth-related media containing misinformation. Thematic analysis revealed three key themes: (1) misinformation in media and social media can influence the type of care individuals seek; (2) students acknowledge the professional responsibility of nurses to address misinformation; and (3) although most participants were able to identify misinformation, many lacked strategies for effectively responding to it (Enslein, 2024 ). Yang et al. conducted semi-structured, in-depth interviews with 22 nursing students in China and found that participants encountered numerous challenges in identifying and responding to health misinformation presented through short videos. Furthermore, factors such as the characteristics of the digital platform, the nature of the content, and individual student attributes were found to significantly influence their ability to recognize misinformation (Yang et al., 2024 ). This gap underscores the need for rigorous, valid studies to evaluate levels of health misinformation among nursing students, which could inform targeted educational interventions and policy development. The absence of comprehensive quantitative data not only restricts our understanding of the phenomenon but also hampers efforts to design evidence-based strategies for mitigating its impact on future healthcare professionals. In this context, we conducted a study aimed at measuring the levels of health misinformation among nursing students and identifying predictors of susceptibility to online health misinformation. Specifically, we examined the influence of various demographic factors and the degree of trust in scientists on students’ vulnerability to misinformation encountered online. Methods Study design A cross-sectional study was conducted in Greece, with data collected through an online survey in October 2025. The survey instrument was created using Google Forms and distributed via Facebook and Instagram groups specifically targeting nursing students. This recruitment strategy resulted in a convenience sample. To be eligible for inclusion, participants were required to meet the following criteria: (1) be currently undergraduate nursing students in universities in Greece, (2) spend a minimum of 30 minutes per day on the internet or social media, and (3) provide informed consent. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Von Elm et al., 2008 ). We used G*Power v.3.1.9.2 to calculate our sample size. We included eight predictors in our multivariable models. Thus, considering an anticipated effect size of 0.04 between each independent variable and each outcome, a statistical power of 95%, and a margin of error of 5%, the sample size was estimated to be 327 nursing students. Measurements We collected data on several demographic characteristics of the nursing students, including gender (male or female) and age (treated as a continuous variable). Participants were also asked to self-assess their financial status using an 11-point scale ranging from 0 (very poor financial status) to 10 (excellent financial status). Additionally, we measured their level of trust in websites to report news accurately, as well as their interest in politics, both using self-assessment scales ranging from 0 (not at all) to 10 (completely). Finally, participants reported the amount of time they spend daily on the internet and social media, recorded as a continuous variable in hours. To assess nursing students’ susceptibility to online health misinformation, we employed the Health-Related Online Misinformation Susceptibility Scale (HR-OMISS) (Katsiroumpa, Konstantakopoulou, et al., 2025 ). This instrument is an adapted version of the Online Misinformation Susceptibility Scale (OMISS) (Katsiroumpa, Moisoglou, Mangoulia, et al., 2025 ), which measures online misinformation susceptibility in general. The HR-OMISS, however, focuses specifically on online health misinformation susceptibility. It consists of nine items, including examples such as: “When you see a health-related post or story that interests you on social media or websites, how often do you check if the post includes reliable links and references such as scientific articles?” and “When you see a health-related post or story that interests you on social media or websites, how often do you check the post for grammatical, spelling, or expression errors?” Responses are recorded on a five-point Likert scale: 5 (never), 4 (rarely), 3 (sometimes), 2 (very often), and 1 (always). The total score is obtained by summing responses to all items, resulting in a range from 9 to 45, with higher scores indicating greater susceptibility to misinformation. According to the developers of the HR-OMISS, a cut-off score of 23 is recommended to differentiate participants with high susceptibility from those with typical levels (Katsiroumpa, Moisoglou, Gallos, et al., 2025 ). In this study, we utilized the validated Greek version of the HR-OMISS. The internal consistency of the scale was high, with a Cronbach’s alpha of 0.860. To evaluate nursing students’ trust in scientists, we employed the Trust in Scientists Scale (TISS), a 12-item instrument that assesses four core dimensions: integrity, competence, benevolence, and openness (Cologna et al., 2025 ). Due to the high correlations among these dimensions (r > 0.8, p-value 0.8, p-value < 0.001). This subscale comprises three items: “How honest or dishonest are most scientists?”, “How ethical or unethical are most scientists?”, and “How sincere or insincere are most scientists?” Responses are rated on a five-point Likert scale, ranging from 1 (very dishonest/unethical/insincere) to 5 (very honest/ethical/sincere). The integrity score was calculated as the mean of the three item responses, yielding a total score between 1 and 5, with higher scores indicating greater trust in scientists. We utilized the validated Greek version of the TISS (Cologna et al., 2025 ) and in our sample, the integrity subscale demonstrated good internal consistency (Cronbach’s alpha = 0.859). Ethical issues The study was conducted in compliance with the principles outlined in the Declaration of Helsinki (“World Medical Association Declaration of Helsinki,” 2013). Ethical approval was obtained from the Ethics Committee of the Faculty of Nursing at the National and Kapodistrian University of Athens (approval No. 75, July 13, 2025). Data collection was carried out anonymously and on a voluntary basis. Nursing students were informed about the purpose and design of the study, and informed consent was obtained from all participants. Statistical analysis Categorical variables were reported as frequencies and percentages. For continuous variables, we presented the mean and standard deviation (SD), as well as the median and interquartile range. The Kolmogorov-Smirnov test and Q-Q plots were employed to assess the distribution of continuous variables, which were found to follow a normal distribution. Independent variables included demographic characteristics and trust in scientists, while the dependent variable was susceptibility to online health misinformation. Given that the dependent variables were continuous and normally distributed, linear regression analysis was conducted. Initially, simple linear regression was performed, followed by the development of a multivariable model to determine the independent effect of each predictor on the outcomes. Both unadjusted and adjusted beta coefficients, along with 95% confidence intervals (CI) and p-values, were reported. Pearson’s correlation coefficient was used to examine the correlations among the study scales, as their scores were normally distributed. A p-value of less than 0.05 was considered indicative of statistical significance. We used the IBM SPSS 28.0 (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp) for the analysis. Results Demographic characteristics Our sample included 388 nursing students. Most of them were females (77.1%). Mean age was 20.50 years (SD; 1.38) with a median of 21 years (interquartile range; 1 year). Mean score of financial status was 5.29 (SD; 1.88) while median score was 6 (interquartile range; 3). Mean trust score in websites was 3.93 (SD; 1.98) with a median of 4 (interquartile range; 2). Mean score of interest in politics was 5.15 (SD; 2.79), while median score was 5 (interquartile range; 5). Mean daily time in web/social media was 4.51 hours (SD; 2.63) with a median of 4.0 hours (interquartile range; 2 hours). Demographic characteristics of our sample are shown in Table 1 . Table 1 Demographic characteristics of the study sample (n = 388). Characteristics N % Gender Males 89 22.9 Females 299 77.1 Age a 20.50 1.38 Financial status a 5.29 1.88 Trust in websites a 3.93 1.98 Interest in politics a 5.15 2.79 Daily time in web/social media (hours) a 4.51 2.63 a mean, standard deviation Study scales Mean score on HR-OMISS was 29.46 (SD; 7.39). Also, 82.7% (n = 321) of our nursing students exhibited a high level of susceptibility to online health misinformation. Mean score on TISS was 3.22 (SD; 0.62). Descriptive statistics for our scales are shown in Table 2 . In our sample, online misinformation susceptibility was negatively correlated with trust in scientists (r = -0.338, p-value < 0.001). Table 2 Descriptive statistics for the study scales (n = 388). Scale Mean Standard deviation Median Interquartile range Health-related Online Misinformation Susceptibility Scale 29.46 7.39 30 10 Trust in Scientists Scale 3.22 0.62 3 0.67 Dependent variable: online health misinformation susceptibility After adjustment for confounders, we found that nursing students with lower trust in scientists had also higher levels of online health misinformation susceptibility (adjusted coefficient beta = -2.890, 95% CI = -3.924 to -1.856, p < 0.001). Moreover, lower financial status (adjusted coefficient beta = -0.530, 95% CI = -0.878 to -0.182, p = 0.003) and lower interest in politics (adjusted coefficient beta = -0.830, 95% CI = -1.060 to -0.600, p < 0.001) were associated with increased online health misinformation susceptibility. Our analysis showed a positive association between daily time in web/social media and misinformation susceptibility (adjusted coefficient beta = 0.812, 95% CI = 0.575 to 1.049, p < 0.001). Results from linear regression analyses are shown in Table 3 . Table 3 Linear regression models with Health-related Online Misinformation Susceptibility Scale as the dependent variable (n = 388). Independent variables Univariate models Multivariable model a Unadjusted coefficient beta 95% CI for beta P-value Adjusted coefficient beta 95% CI for beta P-value Females vs. males 2.137 0.393 to 3.882 0.016 1.257 -0.193 to 2.706 0.089 Age 0.137 -0.398 to 0.671 0.615 0.131 -0.317 to 0.579 0.566 Financial status -1.001 -1.382 to -0.620 < 0.001 -0.530 -0.878 to -0.182 0.003 Trust in websites -0.399 -0.772 to -0.027 0.036 0.329 -0.014 to 0.671 0.060 Interest in politics -1.051 -1.295 to -0.807 < 0.001 -0.830 -1.060 to -0.600 < 0.001 Daily time in web/social media 0.984 0.721 to 1.247 < 0.001 0.812 0.575 to 1.049 < 0.001 TISS score -4.012 -5.131 to -2.893 < 0.001 -2.890 -3.924 to -1.856 < 0.001 a R 2 for the multivariable model = 33.4%, p-value for ANOVA < 0.001 CI: confidence interval; TISS: Trust in Scientists Scale Discussion To the best of our knowledge, this study represents the first attempt to employ a validated instrument for measuring levels of online health misinformation among nursing students. Previous research on this topic included only two qualitative studies, exploring perceptions and experiences rather than systematically quantifying susceptibility (Enslein, 2024 ; Yang et al., 2024 ). By utilizing a valid scale, our study provides a more robust and objective assessment of misinformation vulnerability within this population. Furthermore, we examined the role of demographic characteristics—such as age, gender, and socioeconomic status—alongside trust in scientists as potential predictors of susceptibility. Investigating these factors is critical for understanding the underlying determinants of misinformation vulnerability and for informing targeted educational interventions aimed at strengthening digital literacy and critical appraisal skills among future healthcare professionals. Our analysis revealed that a substantial proportion of nursing students demonstrated high susceptibility to online health misinformation, with 82.7% classified within this category. This finding indicates that the majority of nursing students in the sample are vulnerable to inaccurate health information encountered on digital platforms. Such a high prevalence raises concerns regarding the potential influence of misinformation on students’ knowledge base and future clinical decision-making. The result aligns with broader evidence suggesting that medical students and healthcare professionals, despite their professional training, are not immune to the cognitive and behavioral effects of misinformation (Denniss & Lindberg, 2025 ; Deori et al., 2025 ). This vulnerability may be attributed to factors such as frequent exposure to unverified content on social media, limited digital literacy, and varying levels of trust in scientific sources (Scherer & Pennycook, 2020 ; Van Der Linden et al., 2025 ). These findings underscore the urgency of integrating targeted interventions within nursing curricula to enhance critical appraisal skills and equip students with strategies to identify and counter misinformation effectively (Denniss & Lindberg, 2025 ). Our study revealed a significant association between trust in scientists and susceptibility to online health misinformation among nursing students. Specifically, students who reported lower levels of trust in scientific authorities exhibited higher susceptibility scores. This finding underscores the role of trust as a foundational element in information processing and decision-making. Trust in science serves as a cognitive anchor that guides individuals toward credible sources and evidence-based practices (Hopkin et al., 2025 ; Horton et al., 2025 ; Rosman & Grösser, 2024 ; Wintterlin et al., 2022 ); when this trust is weakened, students may become more reliant on alternative information channels such as social media, blogs, or peer networks, which often lack rigorous verification (Aïmeur et al., 2023 ; Boothby et al., 2021 ; Westerman et al., 2014 ). Such reliance increases exposure to misinformation and amplifies the risk of adopting inaccurate health beliefs (Denniss & Lindberg, 2025 ; Paul & Headley-Johnson, 2025 ; Schmid et al., 2023 ). Furthermore, diminished trust may reflect broader socio-cultural dynamics, including skepticism toward institutions or perceived inconsistencies in scientific communication, which can erode confidence in expert guidance. These patterns are particularly concerning in healthcare education, where misinformation can directly influence clinical reasoning and patient care. Addressing this issue requires not only strengthening digital literacy but also fostering epistemic trust through transparent communication, critical thinking training, and curriculum strategies that emphasize the value of scientific evidence in professional practice (O’Brien et al., 2021 ; Peter, 2025 ; Van Der Linden et al., 2023 ). We identified a significant association between financial status and susceptibility to online health misinformation among nursing students, with lower financial status linked to higher vulnerability. This association may be explained by several interrelated factors. Students from lower socioeconomic backgrounds often have reduced access to high-quality educational resources and may rely more heavily on freely available information from social media or non-validated online sources, which are common channels for misinformation (Carletto et al., 2025 ; Gondwe et al., 2025 ). Additionally, financial constraints can limit opportunities for advanced digital literacy training, making it harder to critically evaluate health-related content (Ojong, 2025 ). Psychological stress associated with economic hardship may also increase reliance on quick, easily accessible information rather than verified scientific sources. These findings are consistent with broader evidence suggesting that socioeconomic disparities influence health information-seeking behaviors and trust in authoritative sources (Scherer & Pennycook, 2020 ; Van Der Linden et al., 2023 ). Addressing this issue requires targeted interventions that promote equitable access to reliable health information and incorporate digital literacy education into nursing curricula, particularly for students from disadvantaged backgrounds (Denniss & Lindberg, 2025 ). Our findings revealed that nursing students with lower interest in politics exhibited higher susceptibility to online health misinformation. This association may reflect the broader role of civic engagement and critical thinking in information evaluation. Interest in politics often correlates with higher levels of media literacy and analytical skills, as politically engaged individuals tend to consume diverse sources, question narratives, and evaluate evidence critically (Ashley et al., 2017 ; Espeland, 2024 ; Kahne & Bowyer, 2019 ). Conversely, students with limited political interest may demonstrate lower exposure to fact-checking practices and reduced motivation to scrutinize information, making them more vulnerable to misinformation. Additionally, political disengagement can be linked to lower trust in institutions and experts, further amplifying susceptibility to unverified health claims (Coles, 2024 ; Steinfeld & Lev-on, 2024 ). These findings highlight the importance of fostering critical thinking and civic awareness within nursing education, as these competencies contribute to resilience against misinformation and support evidence-based practice (Guess et al., 2020 ; Scherer & Pennycook, 2020 ; Van Der Linden et al., 2023 ). Our study demonstrated that increased daily time spent on the web and social media is positively associated with higher susceptibility to online health misinformation among nursing students. This association can be attributed to the nature of digital environments, where prolonged exposure increases the likelihood of encountering unverified or misleading health content. Social media platforms, in particular, employ algorithms that amplify sensational or emotionally charged information, which often includes misinformation (Denniss & Lindberg, 2025 ). Extended engagement with these platforms may also foster echo chambers, reinforcing inaccurate beliefs through repeated exposure and social validation (Westberry et al., 2023 ). Moreover, frequent online activity does not necessarily equate to improved digital literacy; individuals who spend more time online may still lack the critical appraisal skills needed to differentiate credible sources from unreliable ones. This pattern is consistent with prior research indicating that high social media use correlates with greater misinformation vulnerability, especially when combined with low trust in authoritative sources and limited fact-checking behaviors (Ecker et al., 2025 ; Roozenbeek et al., 2023 ). Several limitations should be acknowledged when interpreting the findings of this study. First, we employed a cross-sectional design, which restricts the ability to infer causal relationships between demographic factors, trust in scientists, and susceptibility to online health misinformation. Longitudinal studies would be necessary to establish temporal dynamics and causality. Second, the sample was drawn from a single population of nursing students, which may limit the generalizability of the results to other healthcare disciplines or students in different cultural and educational contexts. Future studies with random and representative samples in different countries and students would add valuable information. Third, the reliance on self-reported measures introduces the possibility of social desirability bias and inaccuracies in reporting behaviors such as time spent on social media. Fourth, although a validated scale was used to measure misinformation susceptibility, the study did not account for variability in platform algorithms or content exposure, which could influence individual experiences with misinformation. Finally, the study focused primarily on demographic and attitudinal predictors, leaving out other potentially relevant factors such as cognitive styles, digital literacy competencies, and psychological traits, which future research should explore to provide a more comprehensive understanding of misinformation vulnerability. Conclusions Online health misinformation has profound implications for healthcare education, particularly among nursing students. Exposure to inaccurate or misleading health content on digital platforms can create knowledge gaps and foster misconceptions, which may compromise evidence-based practice. During public health crises such as the COVID-19 pandemic, reliance on non-credible sources has been associated with poorer understanding of preventive measures and clinical guidelines (Schmid et al., 2023 ). Beyond cognitive effects, misinformation imposes a psychological burden, increasing uncertainty and stress as students struggle to discern reliable information (Deori et al., 2025 ). This challenge extends to professional responsibilities: while nursing students often recognize misinformation, they frequently lack the skills to effectively counter it in patient interactions, highlighting a critical gap in current curricula (Rolls & Massey, 2021 ). If unchecked, these influences can translate into unsafe clinical decisions and inadequate patient education, undermining public health efforts (Lan et al., 2024 ). Consequently, scholars advocate for integrating digital literacy and misinformation management strategies into nursing education to equip future professionals with the competencies required to navigate the complexities of modern healthcare communication. Declarations Funding None Competing interests None Ethical approval All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study. Conflict of interest All authors declare that they have no conflict of interest. References Adeoye, A. F., Umoru, D. O., Gomez, O. O., Onifade, I. A., Akangbe, B. O., Elechi, U. S., & Barrah, V. U. (2025). The 2025 United States Measles Crisis: When Vaccine Hesitancy Meets Reality. Cureus . https://doi.org/10.7759/cureus.88196 Aïmeur, E., Amri, S., & Brassard, G. (2023). 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Bridging the digital divide: Unmasking socioeconomic barriers to equitable access to digital tools in education. International Journal of Science and Research Archive , 15 (1), 1285–1300. https://doi.org/10.30574/ijsra.2025.15.1.1143 Paul, B., & Headley-Johnson, S.-A. (2025). The Impact of Social Media on Health Behaviors, a Systematic Review. Healthcare , 13 (21), 2763. https://doi.org/10.3390/healthcare13212763 Perfors, A. (2025). Information and Misinformation. In Open Encyclopedia of Cognitive Science (1st ed.). MIT Press. https://doi.org/10.21428/e2759450.a2555670 Peter, E. (2025). Nurses and misinformation: A matter of trust. Nursing Ethics , 09697330251388932. https://doi.org/10.1177/09697330251388932 Pierri, F., Perry, B. L., DeVerna, M. R., Yang, K.-C., Flammini, A., Menczer, F., & Bryden, J. (2022). Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal. Scientific Reports , 12 (1), 5966. https://doi.org/10.1038/s41598-022-10070-w Rau, M. A., & Premo, A. E. (2025). Systematic Review of Educational Approaches to Misinformation. Educational Psychology Review , 37 (2), 43. https://doi.org/10.1007/s10648-025-10012-8 Rodrigues, F., Ziade, N., Jatuworapruk, K., Caballero-Uribe, C. V., Khursheed, T., & Gupta, L. (2023). The Impact of Social Media on Vaccination: A Narrative Review. Journal of Korean Medical Science , 38 (40), e326. https://doi.org/10.3346/jkms.2023.38.e326 Rolls, K., & Massey, D. (2021). Social media is a source of health-related misinformation. Evidence Based Nursing , 24 (2), 46–46. https://doi.org/10.1136/ebnurs-2019-103222 Roozenbeek, J., Culloty, E., & Suiter, J. (2023). Countering Misinformation: Evidence, Knowledge Gaps, and Implications of Current Interventions. European Psychologist , 28 (3), 189–205. https://doi.org/10.1027/1016-9040/a000492 Roozenbeek, J., & Van Der Linden, S. (2024). The Psychology of Misinformation (1st ed.). Cambridge University Press. https://doi.org/10.1017/9781009214414 Rosman, T., & Grösser, S. (2024). Belief updating when confronted with scientific evidence: Examining the role of trust in science. Public Understanding of Science , 33 (3), 308–324. https://doi.org/10.1177/09636625231203538 Scherer, L. D., & Pennycook, G. (2020). Who Is Susceptible to Online Health Misinformation? American Journal of Public Health , 110 (S3), S276–S277. https://doi.org/10.2105/AJPH.2020.305908 Schmid, P., Altay, S., & Scherer, L. D. (2023). The Psychological Impacts and Message Features of Health Misinformation: A Systematic Review of Randomized Controlled Trials. European Psychologist , 28 (3), 162–172. https://doi.org/10.1027/1016-9040/a000494 Steinfeld, N., & Lev-on, A. (2024). Exposure to diverse political views in contemporary media environments. Frontiers in Communication , 9 , 1384706. https://doi.org/10.3389/fcomm.2024.1384706 The Lancet Infectious Diseases. (2025). Facts and myths about measles. The Lancet Infectious Diseases , 25 (4), 357. https://doi.org/10.1016/S1473-3099(25)00164-1 Van Der Linden, S., Albarracín, D., Fazio, L., Freelon, D., Roozenbeek, J., Swire-Thompson, B., & Van Bavel, J. (2023). Using psychological science to understand and fight health misinformation . American Psychological Association. https://www.apa.org/pubs/reports/misinformation-consensus-statement.pdf Van Der Linden, S., Albarracín, D., Fazio, L., Freelon, D., Roozenbeek, J., Swire-Thompson, B., & Van Bavel, J. (2025). Using psychological science to understand and fight health misinformation: An APA consensus statement. American Psychologist . https://doi.org/10.1037/amp0001598 Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2008). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Journal of Clinical Epidemiology , 61 (4), 344–349. https://doi.org/10.1016/j.jclinepi.2007.11.008 Westberry, C., Palmer, X.-L., & Potter, L. (2023). Social Media and Health Misinformation: A Literature Review. In K. Arai (Ed.), Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3 (Vol. 815, pp. 404–418). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47457-6_26 Westerman, D., Spence, P. R., & Van Der Heide, B. (2014). Social Media as Information Source: Recency of Updates and Credibility of Information. Journal of Computer-Mediated Communication , 19 (2), 171–183. https://doi.org/10.1111/jcc4.12041 Wintterlin, F., Hendriks, F., Mede, N. G., Bromme, R., Metag, J., & Schäfer, M. S. (2022). Predicting Public Trust in Science: The Role of Basic Orientations Toward Science, Perceived Trustworthiness of Scientists, and Experiences With Science. Frontiers in Communication , 6 , 822757. https://doi.org/10.3389/fcomm.2021.822757 World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. (2013). JAMA , 310 (20), 2191. https://doi.org/10.1001/jama.2013.281053 Yang, M., Huang, W., Shen, M., Du, J., Wang, L., Zhang, Y., Xia, Q., Yang, J., Fu, Y., Mao, Q., Pan, M., Huangfu, Z., Wang, F., & Zhu, W. (2024). Qualitative research on undergraduate nursing students’ recognition and response to short videos’ health disinformation. Heliyon , 10 (15), e35455. https://doi.org/10.1016/j.heliyon.2024.e35455 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Greece\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnlike disinformation, which is deliberately fabricated to mislead, misinformation often arises from misunderstanding, misinterpretation, or lack of verification before dissemination. It encompasses a wide range of content, including inaccurate news articles, pseudoscientific claims, conspiracy theories, and health-related myths circulating on social media, websites, and other online channels (Altay, Berriche, Heuer, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Roozenbeek \u0026amp; Van Der Linden, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, Altay et al. define misinformation as \u0026ldquo;false and misleading information\u0026rdquo;, noting that it includes pseudoscience and conspiracy theories but excludes satire or parody content (Altay, Berriche, \u0026amp; Acerbi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, Broda and Str\u0026ouml;mb\u0026auml;ck emphasize that misinformation has become increasingly prevalent in the digital era, particularly during major sociopolitical events, contributing to what some scholars call a \u0026ldquo;misinformation age\u0026rdquo; (Broda \u0026amp; Str\u0026ouml;mb\u0026auml;ck, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, online misinformation refers to false or misleading information that is shared through digital platforms without the intent to deceive, although it can still cause harm by shaping beliefs, attitudes, or behaviors.\u003c/p\u003e\u003cp\u003eMisinformation can be understood as a multifaceted phenomenon characterized by three core attributes. First, it involves factual inaccuracy, wherein the information disseminated is objectively false or misleading. This may include fabricated data, distorted interpretations, or erroneous claims that lack empirical support (Lewandowsky et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Roozenbeek et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, misinformation is often spread unintentionally; individuals who share such content typically believe it to be accurate, which distinguishes it from deliberate disinformation (Perfors, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, misinformation carries the potential for harm, as it can shape public opinion, influence decision-making processes, and erode trust in institutions (Ecker et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Roozenbeek et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHealth misinformation refers to false, inaccurate, or misleading information related to health topics, shared without intent to deceive, often by individuals who believe the information to be true (Office of the U.S. Surgeon General, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The impact of health misinformation on public health is profound. For instance, during the COVID-19 pandemic, misinformation led to vaccine hesitancy, rejection of public health measures, and adoption of unproven treatments, contributing to preventable illness and death (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pierri et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, one widely circulated false claim suggested that drinking methanol could cure coronavirus, resulting in hundreds of deaths and thousands of hospitalizations globally (Islam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mousavi-Roknabadi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Beyond acute crises, misinformation about routine health practices\u0026mdash;such as the safety of childhood vaccines\u0026mdash;has fueled outbreaks of previously controlled diseases like measles (Adeoye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; The Lancet Infectious Diseases, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, health misinformation erodes trust in medical institutions and professionals, undermines adherence to evidence-based treatments, and exacerbates health inequities. Vulnerable populations, including those with lower health literacy or higher medical mistrust, are disproportionately affected, making misinformation a driver of inequitable health outcomes (Carletto et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lubej \u0026amp; Kirbiš, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese instances underscore the pervasive and consequential nature of misinformation across domains including health, highlighting the need for robust strategies to detect and mitigate its impact (Broda \u0026amp; Str\u0026ouml;mb\u0026auml;ck, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rau \u0026amp; Premo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, digital platforms amplify this problem by enabling rapid, large-scale dissemination of misleading content, often through algorithms that prioritize engagement over accuracy. Thus, exposure to inaccurate or misleading health content on digital platforms can create knowledge gaps and foster misconceptions, which may compromise evidence-based practice.\u003c/p\u003e\u003cp\u003eHowever, the body of literature addressing the measurement of health misinformation among nursing students is notably limited. Despite the growing recognition of misinformation as a critical challenge in healthcare education, empirical research quantifying its prevalence or assessing susceptibility within this population remains scarce. To date, only two qualitative studies have explored selected dimensions of health misinformation among nursing students, focusing primarily on perceptions and experiences rather than systematic measurement (Enslein, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, Enslein conducted a qualitative study involving 14 nursing students who were recruited to watch and listen to birth-related media containing misinformation. Thematic analysis revealed three key themes: (1) misinformation in media and social media can influence the type of care individuals seek; (2) students acknowledge the professional responsibility of nurses to address misinformation; and (3) although most participants were able to identify misinformation, many lacked strategies for effectively responding to it (Enslein, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yang et al. conducted semi-structured, in-depth interviews with 22 nursing students in China and found that participants encountered numerous challenges in identifying and responding to health misinformation presented through short videos. Furthermore, factors such as the characteristics of the digital platform, the nature of the content, and individual student attributes were found to significantly influence their ability to recognize misinformation (Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis gap underscores the need for rigorous, valid studies to evaluate levels of health misinformation among nursing students, which could inform targeted educational interventions and policy development. The absence of comprehensive quantitative data not only restricts our understanding of the phenomenon but also hampers efforts to design evidence-based strategies for mitigating its impact on future healthcare professionals. In this context, we conducted a study aimed at measuring the levels of health misinformation among nursing students and identifying predictors of susceptibility to online health misinformation. Specifically, we examined the influence of various demographic factors and the degree of trust in scientists on students\u0026rsquo; vulnerability to misinformation encountered online.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted in Greece, with data collected through an online survey in October 2025. The survey instrument was created using Google Forms and distributed via Facebook and Instagram groups specifically targeting nursing students. This recruitment strategy resulted in a convenience sample. To be eligible for inclusion, participants were required to meet the following criteria: (1) be currently undergraduate nursing students in universities in Greece, (2) spend a minimum of 30 minutes per day on the internet or social media, and (3) provide informed consent. The study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Von Elm et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We used G*Power v.3.1.9.2 to calculate our sample size. We included eight predictors in our multivariable models. Thus, considering an anticipated effect size of 0.04 between each independent variable and each outcome, a statistical power of 95%, and a margin of error of 5%, the sample size was estimated to be 327 nursing students.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eWe collected data on several demographic characteristics of the nursing students, including gender (male or female) and age (treated as a continuous variable). Participants were also asked to self-assess their financial status using an 11-point scale ranging from 0 (very poor financial status) to 10 (excellent financial status). Additionally, we measured their level of trust in websites to report news accurately, as well as their interest in politics, both using self-assessment scales ranging from 0 (not at all) to 10 (completely). Finally, participants reported the amount of time they spend daily on the internet and social media, recorded as a continuous variable in hours.\u003c/p\u003e\u003cp\u003eTo assess nursing students\u0026rsquo; susceptibility to online health misinformation, we employed the Health-Related Online Misinformation Susceptibility Scale (HR-OMISS) (Katsiroumpa, Konstantakopoulou, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This instrument is an adapted version of the Online Misinformation Susceptibility Scale (OMISS) (Katsiroumpa, Moisoglou, Mangoulia, et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which measures online misinformation susceptibility in general. The HR-OMISS, however, focuses specifically on online health misinformation susceptibility. It consists of nine items, including examples such as: \u0026ldquo;When you see a health-related post or story that interests you on social media or websites, how often do you check if the post includes reliable links and references such as scientific articles?\u0026rdquo; and \u0026ldquo;When you see a health-related post or story that interests you on social media or websites, how often do you check the post for grammatical, spelling, or expression errors?\u0026rdquo; Responses are recorded on a five-point Likert scale: 5 (never), 4 (rarely), 3 (sometimes), 2 (very often), and 1 (always). The total score is obtained by summing responses to all items, resulting in a range from 9 to 45, with higher scores indicating greater susceptibility to misinformation. According to the developers of the HR-OMISS, a cut-off score of 23 is recommended to differentiate participants with high susceptibility from those with typical levels (Katsiroumpa, Moisoglou, Gallos, et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this study, we utilized the validated Greek version of the HR-OMISS. The internal consistency of the scale was high, with a Cronbach\u0026rsquo;s alpha of 0.860.\u003c/p\u003e\u003cp\u003eTo evaluate nursing students\u0026rsquo; trust in scientists, we employed the Trust in Scientists Scale (TISS), a 12-item instrument that assesses four core dimensions: integrity, competence, benevolence, and openness (Cologna et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Due to the high correlations among these dimensions (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), we selected the integrity subscale as a representative measure for the purposes of this study (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This subscale comprises three items: \u0026ldquo;How honest or dishonest are most scientists?\u0026rdquo;, \u0026ldquo;How ethical or unethical are most scientists?\u0026rdquo;, and \u0026ldquo;How sincere or insincere are most scientists?\u0026rdquo; Responses are rated on a five-point Likert scale, ranging from 1 (very dishonest/unethical/insincere) to 5 (very honest/ethical/sincere). The integrity score was calculated as the mean of the three item responses, yielding a total score between 1 and 5, with higher scores indicating greater trust in scientists. We utilized the validated Greek version of the TISS (Cologna et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and in our sample, the integrity subscale demonstrated good internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.859).\u003c/p\u003e\n\u003ch3\u003eEthical issues\u003c/h3\u003e\n\u003cp\u003e The study was conducted in compliance with the principles outlined in the Declaration of Helsinki (\u0026ldquo;World Medical Association Declaration of Helsinki,\u0026rdquo; 2013). Ethical approval was obtained from the Ethics Committee of the Faculty of Nursing at the National and Kapodistrian University of Athens (approval No. 75, July 13, 2025). Data collection was carried out anonymously and on a voluntary basis. Nursing students were informed about the purpose and design of the study, and informed consent was obtained from all participants.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were reported as frequencies and percentages. For continuous variables, we presented the mean and standard deviation (SD), as well as the median and interquartile range. The Kolmogorov-Smirnov test and Q-Q plots were employed to assess the distribution of continuous variables, which were found to follow a normal distribution. Independent variables included demographic characteristics and trust in scientists, while the dependent variable was susceptibility to online health misinformation. Given that the dependent variables were continuous and normally distributed, linear regression analysis was conducted. Initially, simple linear regression was performed, followed by the development of a multivariable model to determine the independent effect of each predictor on the outcomes. Both unadjusted and adjusted beta coefficients, along with 95% confidence intervals (CI) and p-values, were reported. Pearson\u0026rsquo;s correlation coefficient was used to examine the correlations among the study scales, as their scores were normally distributed. A p-value of less than 0.05 was considered indicative of statistical significance. We used the IBM SPSS 28.0 (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp) for the analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDemographic characteristics\u003c/h2\u003e\u003cp\u003eOur sample included 388 nursing students. Most of them were females (77.1%). Mean age was 20.50 years (SD; 1.38) with a median of 21 years (interquartile range; 1 year). Mean score of financial status was 5.29 (SD; 1.88) while median score was 6 (interquartile range; 3). Mean trust score in websites was 3.93 (SD; 1.98) with a median of 4 (interquartile range; 2). Mean score of interest in politics was 5.15 (SD; 2.79), while median score was 5 (interquartile range; 5). Mean daily time in web/social media was 4.51 hours (SD; 2.63) with a median of 4.0 hours (interquartile range; 2 hours). Demographic characteristics of our sample are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of the study sample (n\u0026thinsp;=\u0026thinsp;388).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial status\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrust in websites\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterest in politics\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily time in web/social media (hours)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e mean, standard deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy scales\u003c/h3\u003e\n\u003cp\u003eMean score on HR-OMISS was 29.46 (SD; 7.39). Also, 82.7% (n\u0026thinsp;=\u0026thinsp;321) of our nursing students exhibited a high level of susceptibility to online health misinformation. Mean score on TISS was 3.22 (SD; 0.62). Descriptive statistics for our scales are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn our sample, online misinformation susceptibility was negatively correlated with trust in scientists (r = -0.338, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for the study scales (n\u0026thinsp;=\u0026thinsp;388).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterquartile range\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth-related Online Misinformation Susceptibility Scale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrust in Scientists Scale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eDependent variable: online health misinformation susceptibility\u003c/h3\u003e\n\u003cp\u003eAfter adjustment for confounders, we found that nursing students with lower trust in scientists had also higher levels of online health misinformation susceptibility (adjusted coefficient beta = -2.890, 95% CI = -3.924 to -1.856, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, lower financial status (adjusted coefficient beta = -0.530, 95% CI = -0.878 to -0.182, p\u0026thinsp;=\u0026thinsp;0.003) and lower interest in politics (adjusted coefficient beta = -0.830, 95% CI = -1.060 to -0.600, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with increased online health misinformation susceptibility. Our analysis showed a positive association between daily time in web/social media and misinformation susceptibility (adjusted coefficient beta\u0026thinsp;=\u0026thinsp;0.812, 95% CI\u0026thinsp;=\u0026thinsp;0.575 to 1.049, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Results from linear regression analyses are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear regression models with Health-related Online Misinformation Susceptibility Scale as the dependent variable (n\u0026thinsp;=\u0026thinsp;388).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndependent variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariate models\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariable model\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnadjusted coefficient beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI for beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted coefficient beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI for beta\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales vs. males\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.393 to 3.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.193 to 2.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.398 to 0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.317 to 0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.382 to -0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.878 to -0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrust in websites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.772 to -0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.014 to 0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterest in politics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.295 to -0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.060 to -0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily time in web/social media\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.721 to 1.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.575 to 1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTISS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.131 to -2.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.924 to -1.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e R\u003csup\u003e2\u003c/sup\u003e for the multivariable model\u0026thinsp;=\u0026thinsp;33.4%, p-value for ANOVA\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eCI: confidence interval; TISS: Trust in Scientists Scale\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study represents the first attempt to employ a validated instrument for measuring levels of online health misinformation among nursing students. Previous research on this topic included only two qualitative studies, exploring perceptions and experiences rather than systematically quantifying susceptibility (Enslein, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By utilizing a valid scale, our study provides a more robust and objective assessment of misinformation vulnerability within this population. Furthermore, we examined the role of demographic characteristics\u0026mdash;such as age, gender, and socioeconomic status\u0026mdash;alongside trust in scientists as potential predictors of susceptibility. Investigating these factors is critical for understanding the underlying determinants of misinformation vulnerability and for informing targeted educational interventions aimed at strengthening digital literacy and critical appraisal skills among future healthcare professionals.\u003c/p\u003e\u003cp\u003eOur analysis revealed that a substantial proportion of nursing students demonstrated high susceptibility to online health misinformation, with 82.7% classified within this category. This finding indicates that the majority of nursing students in the sample are vulnerable to inaccurate health information encountered on digital platforms. Such a high prevalence raises concerns regarding the potential influence of misinformation on students\u0026rsquo; knowledge base and future clinical decision-making. The result aligns with broader evidence suggesting that medical students and healthcare professionals, despite their professional training, are not immune to the cognitive and behavioral effects of misinformation (Denniss \u0026amp; Lindberg, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Deori et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This vulnerability may be attributed to factors such as frequent exposure to unverified content on social media, limited digital literacy, and varying levels of trust in scientific sources (Scherer \u0026amp; Pennycook, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Der Linden et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings underscore the urgency of integrating targeted interventions within nursing curricula to enhance critical appraisal skills and equip students with strategies to identify and counter misinformation effectively (Denniss \u0026amp; Lindberg, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study revealed a significant association between trust in scientists and susceptibility to online health misinformation among nursing students. Specifically, students who reported lower levels of trust in scientific authorities exhibited higher susceptibility scores. This finding underscores the role of trust as a foundational element in information processing and decision-making. Trust in science serves as a cognitive anchor that guides individuals toward credible sources and evidence-based practices (Hopkin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Horton et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rosman \u0026amp; Gr\u0026ouml;sser, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wintterlin et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); when this trust is weakened, students may become more reliant on alternative information channels such as social media, blogs, or peer networks, which often lack rigorous verification (A\u0026iuml;meur et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Boothby et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Westerman et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such reliance increases exposure to misinformation and amplifies the risk of adopting inaccurate health beliefs (Denniss \u0026amp; Lindberg, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Paul \u0026amp; Headley-Johnson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Schmid et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, diminished trust may reflect broader socio-cultural dynamics, including skepticism toward institutions or perceived inconsistencies in scientific communication, which can erode confidence in expert guidance. These patterns are particularly concerning in healthcare education, where misinformation can directly influence clinical reasoning and patient care. Addressing this issue requires not only strengthening digital literacy but also fostering epistemic trust through transparent communication, critical thinking training, and curriculum strategies that emphasize the value of scientific evidence in professional practice (O\u0026rsquo;Brien et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peter, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Van Der Linden et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe identified a significant association between financial status and susceptibility to online health misinformation among nursing students, with lower financial status linked to higher vulnerability. This association may be explained by several interrelated factors. Students from lower socioeconomic backgrounds often have reduced access to high-quality educational resources and may rely more heavily on freely available information from social media or non-validated online sources, which are common channels for misinformation (Carletto et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gondwe et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, financial constraints can limit opportunities for advanced digital literacy training, making it harder to critically evaluate health-related content (Ojong, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Psychological stress associated with economic hardship may also increase reliance on quick, easily accessible information rather than verified scientific sources. These findings are consistent with broader evidence suggesting that socioeconomic disparities influence health information-seeking behaviors and trust in authoritative sources (Scherer \u0026amp; Pennycook, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Der Linden et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Addressing this issue requires targeted interventions that promote equitable access to reliable health information and incorporate digital literacy education into nursing curricula, particularly for students from disadvantaged backgrounds (Denniss \u0026amp; Lindberg, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur findings revealed that nursing students with lower interest in politics exhibited higher susceptibility to online health misinformation. This association may reflect the broader role of civic engagement and critical thinking in information evaluation. Interest in politics often correlates with higher levels of media literacy and analytical skills, as politically engaged individuals tend to consume diverse sources, question narratives, and evaluate evidence critically (Ashley et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Espeland, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kahne \u0026amp; Bowyer, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Conversely, students with limited political interest may demonstrate lower exposure to fact-checking practices and reduced motivation to scrutinize information, making them more vulnerable to misinformation. Additionally, political disengagement can be linked to lower trust in institutions and experts, further amplifying susceptibility to unverified health claims (Coles, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Steinfeld \u0026amp; Lev-on, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings highlight the importance of fostering critical thinking and civic awareness within nursing education, as these competencies contribute to resilience against misinformation and support evidence-based practice (Guess et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Scherer \u0026amp; Pennycook, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Der Linden et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study demonstrated that increased daily time spent on the web and social media is positively associated with higher susceptibility to online health misinformation among nursing students. This association can be attributed to the nature of digital environments, where prolonged exposure increases the likelihood of encountering unverified or misleading health content. Social media platforms, in particular, employ algorithms that amplify sensational or emotionally charged information, which often includes misinformation (Denniss \u0026amp; Lindberg, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Extended engagement with these platforms may also foster echo chambers, reinforcing inaccurate beliefs through repeated exposure and social validation (Westberry et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, frequent online activity does not necessarily equate to improved digital literacy; individuals who spend more time online may still lack the critical appraisal skills needed to differentiate credible sources from unreliable ones. This pattern is consistent with prior research indicating that high social media use correlates with greater misinformation vulnerability, especially when combined with low trust in authoritative sources and limited fact-checking behaviors (Ecker et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Roozenbeek et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged when interpreting the findings of this study. First, we employed a cross-sectional design, which restricts the ability to infer causal relationships between demographic factors, trust in scientists, and susceptibility to online health misinformation. Longitudinal studies would be necessary to establish temporal dynamics and causality. Second, the sample was drawn from a single population of nursing students, which may limit the generalizability of the results to other healthcare disciplines or students in different cultural and educational contexts. Future studies with random and representative samples in different countries and students would add valuable information. Third, the reliance on self-reported measures introduces the possibility of social desirability bias and inaccuracies in reporting behaviors such as time spent on social media. Fourth, although a validated scale was used to measure misinformation susceptibility, the study did not account for variability in platform algorithms or content exposure, which could influence individual experiences with misinformation. Finally, the study focused primarily on demographic and attitudinal predictors, leaving out other potentially relevant factors such as cognitive styles, digital literacy competencies, and psychological traits, which future research should explore to provide a more comprehensive understanding of misinformation vulnerability.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOnline health misinformation has profound implications for healthcare education, particularly among nursing students. Exposure to inaccurate or misleading health content on digital platforms can create knowledge gaps and foster misconceptions, which may compromise evidence-based practice. During public health crises such as the COVID-19 pandemic, reliance on non-credible sources has been associated with poorer understanding of preventive measures and clinical guidelines (Schmid et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond cognitive effects, misinformation imposes a psychological burden, increasing uncertainty and stress as students struggle to discern reliable information (Deori et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This challenge extends to professional responsibilities: while nursing students often recognize misinformation, they frequently lack the skills to effectively counter it in patient interactions, highlighting a critical gap in current curricula (Rolls \u0026amp; Massey, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If unchecked, these influences can translate into unsafe clinical decisions and inadequate patient education, undermining public health efforts (Lan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, scholars advocate for integrating digital literacy and misinformation management strategies into nursing education to equip future professionals with the competencies required to navigate the complexities of modern healthcare communication.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eAll procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli dir=\"LTR\"\u003eAdeoye, A. F., Umoru, D. O., Gomez, O. O., Onifade, I. A., Akangbe, B. O., Elechi, U. S., \u0026amp; Barrah, V. U. (2025). The 2025 United States Measles Crisis: When Vaccine Hesitancy Meets Reality. \u003cem\u003eCureus\u003c/em\u003e. https://doi.org/10.7759/cureus.88196\u003c/li\u003e\n \u003cli dir=\"LTR\"\u003eA\u0026iuml;meur, E., Amri, S., \u0026amp; Brassard, G. (2023). 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Predicting Public Trust in Science: The Role of Basic Orientations Toward Science, Perceived Trustworthiness of Scientists, and Experiences With Science. \u003cem\u003eFrontiers in Communication\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 822757. https://doi.org/10.3389/fcomm.2021.822757\u003c/li\u003e\n \u003cli dir=\"LTR\"\u003eWorld Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. (2013). \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e310\u003c/em\u003e(20), 2191. https://doi.org/10.1001/jama.2013.281053\u003c/li\u003e\n \u003cli dir=\"LTR\"\u003eYang, M., Huang, W., Shen, M., Du, J., Wang, L., Zhang, Y., Xia, Q., Yang, J., Fu, Y., Mao, Q., Pan, M., Huangfu, Z., Wang, F., \u0026amp; Zhu, W. (2024). Qualitative research on undergraduate nursing students\u0026rsquo; recognition and response to short videos\u0026rsquo; health disinformation. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(15), e35455. https://doi.org/10.1016/j.heliyon.2024.e35455\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National and Kapodistrian University of Athens","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"health misinformation, susceptibility, nursing students, scientists, trust, fake news","lastPublishedDoi":"10.21203/rs.3.rs-8149358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8149358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGiven the recent surge in online health misinformation, it is essential to assess the vulnerability of nursing students to this phenomenon.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo identify predictors of online health misinformation susceptibility in nursing students. In particular, we examined the impact of several demographic variables and trust in scientists on online health misinformation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted in Greece, with data collected through an online survey in October 2025. We collected data on several demographic characteristics such as gender, age, financial status, level of trust in websites, interest in politics, and the amount of time participants spend daily on the internet and social media. To assess nursing students\u0026rsquo; susceptibility to online health misinformation, we employed the Health-Related Online Misinformation Susceptibility Scale. Trust in scientists was assessed using the Trust in Scientists Scale.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur sample included 388 nursing students with a mean age of 21 years. Among our students, 82.7% exhibited a high level of susceptibility to online health misinformation. Multivariable analysis showed that students with lower trust in scientists had also higher levels of online health misinformation susceptibility. Moreover, lower financial status and lower interest in politics were associated with increased online health misinformation susceptibility. Our analysis showed a positive association between daily time in web/social media and online health misinformation susceptibility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eA substantial proportion of nursing students demonstrated a high susceptibility to online health misinformation. Factors such as trust in scientists, financial status, interest in politics, and the amount of time spent daily on web and social media platforms were found to influence this susceptibility.\u003c/p\u003e","manuscriptTitle":"Predictors of online health misinformation susceptibility among nursing students in Greece","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 12:10:41","doi":"10.21203/rs.3.rs-8149358/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70d5482e-4a59-46b0-b3b2-0c2d0cbd3c8d","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-20T12:10:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 12:10:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8149358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8149358","identity":"rs-8149358","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}