Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Sep 2;73(3):e70235. doi: 10.1111/inr.70235

Factors Associated With Nurses’ Self‐Reported Susceptibility to Online Health Misinformation: Evidence From Greece

Aglaia Katsiroumpa 1, Ioannis Moisoglou 2, Olympia Konstantakopoulou 1, Petros Galanis 1,✉
PMCID: PMC13535901  PMID: 42683609

ABSTRACT

Aim

We investigated factors associated with online health misinformation in nurses. In particular, we examined the association between several demographic variables, nurses’ perceived integrity of scientists, and online health misinformation susceptibility.

Background

The ability to identify and counteract misinformation in healthcare is more critical than ever to safeguard public health and maintain confidence in evidence‐based practices.

Methods

For this cross sectional study, we collected our data in Greece through an online survey during October 2025. We used the Health‐Related Online Misinformation Susceptibility Scale to measure online health misinformation behavioral susceptibility in our nurses. We used the Trust in Scientists Scale to measure levels of nurses’ perceived integrity of scientists. Our study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Results

We found that nurses’ perceived integrity of scientists is associated with online health misinformation behavioral susceptibility. Moreover, we found that nurses with an MSc/PhD diploma had lower levels of misinformation susceptibility. Additionally, interest in politics was associated with lower misinformation susceptibility.

Conclusion

Our findings showed a negative association between nurses’ perceived integrity of scientists and online health misinformation susceptibility. Additionally, educational level and interest in politics were associated with online health misinformation susceptibility.

Implications for Nursing

Identification of factors associated with nurses’ misinformation susceptibility is crucial to identify high‐risk groups and develop appropriate strategies to reduce this phenomenon. In this context, policymakers and healthcare organizations should reduce nurses’ misinformation susceptibility and improve their ability to detect fake news.

Implications for health policy

Well‐informed nurses are essential for providing the public with accurate and reliable information. In this way, public health can be promoted, and individuals’ quality of life can be improved.

Keywords: fake news, health misinformation, medical misinformation, nurses, scientists, susceptibility, trust

1. Background

The digital era has revolutionized health information dissemination, with social media and online platforms becoming primary sources for both patients and healthcare professionals (Rolls and Massey 2021). While these channels offer unprecedented access to medical knowledge, they also facilitate the rapid spread of misinformation, posing significant risks to public health and clinical practice (Wang et al. 2019). Health misinformation—defined as false or misleading health‐related claims not supported by scientific evidence—has been linked to vaccine hesitancy, poor treatment adherence, and erosion of trust in healthcare systems (Kbaier et al. 2024). It is important to distinguish between health misinformation and medical misinformation, as the latter refers specifically to false, inaccurate, or misleading information related to medical science, clinical practice, diagnosis, or treatment (Arora et al. 2020). Medical misinformation can therefore be considered a subset of the broader concept of health misinformation. Although both forms of misinformation have the potential to influence health‐related outcomes, health misinformation encompasses a wider range of topics, including social, behavioral, public health, and environmental determinants of health. In contrast, medical misinformation is confined to information pertaining to biomedical knowledge, clinical interventions, diagnostic procedures, and therapeutic practices (Khullar 2022). Specifically, health misinformation may include inaccurate or misleading claims related to health promotion, disease prevention, nutrition, vaccination, mental health, environmental health risks, lifestyle behaviors, public health interventions, and social determinants of health. Such misinformation can influence individual health‐related decisions, shape public attitudes toward health policies, and affect the adoption of preventive behaviors. In contrast, medical misinformation is more narrowly focused on biomedical and clinical issues, including disease etiology, diagnostic procedures, treatment options, medication use, and the interpretation of scientific and clinical evidence.

Studies indicate that misinformation spreads faster and more broadly than accurate information on social media, driven by algorithms prioritizing engagement over accuracy and amplified by emotionally charged content. For instance, during the COVID‐19 pandemic, misinformation about vaccines and treatments dominated online discourse, influencing patient decisions and creating additional burdens for healthcare professionals, including nurses, who often serve as trusted sources of health guidance (Lee et al. 2022; Pierri et al. 2022; Rodrigues et al. 2023).

Nurses, often regarded as trusted health professionals, play a critical role in patient education and evidence‐based care. However, research indicates that nurses themselves can be susceptible to health misinformation due to information overload, low digital health literacy and professional health literacy, and cognitive biases. Algorithm‐driven echo chambers and confirmation bias reinforce false beliefs, making it harder for nurses to discern credible sources (Deori et al. 2025; Sharman 2023). The proliferation of online health misinformation poses significant challenges for healthcare professionals, including nurses, who play a pivotal role in patient education and evidence‐based practice.

The COVID‐19 pandemic was accompanied by an unprecedented surge in health misinformation, leading the World Health Organization (WHO) to describe the phenomenon as an “infodemic,” i.e., a situation where excessive amounts of accurate and inaccurate information circulate simultaneously, making it difficult for individuals to identify trustworthy sources (The Lancet Infectious Diseases 2020). In particular, social media platforms played a central role in accelerating the spread of misinformation. Algorithmic systems designed to maximize engagement often prioritized sensational or emotionally charged content over verified information, enabling false narratives to reach global audiences rapidly (Cinelli, Quattrociocchi, et al. 2020). Automated bots and influencers further amplified misleading claims, creating echo chambers that reinforced misinformation (Islam et al. 2020).

Thus, the COVID‐19 pandemic illustrates the profound influence of the contemporary information ecosystem. The rapid dissemination of content significantly shapes individual behaviors and can undermine the effectiveness of government interventions. Social media platforms grant users direct access to vast amounts of information, often amplifying rumors and questionable claims. Algorithms, designed to reflect user preferences and attitudes, facilitate the promotion and circulation of content, thereby accelerating information spread (Kulshrestha et al. 2017). This departure from traditional news paradigms has far‐reaching implications for the formation of social perceptions and narrative framing, influencing policy‐making, political discourse, and the trajectory of public debate—particularly on contentious issues (Del Vicario, Bessi, et al. 2016; Schmidt et al. 2017; Starnini et al. 2016). Online users frequently seek information that aligns with their existing beliefs, disregard opposing viewpoints, and form polarized communities around shared narratives (Baronchelli 2018; Cinelli et al. 2020). Moreover, in highly polarized environments, misinformation can proliferate with ease (Bail et al. 2018; Del Vicario et al. 2016).

In this context, online health misinformation is a significant concern for nurses because it directly impacts their core roles as healthcare providers and patient educators. Nurses occupy a unique position as frontline educators and advocates for evidence‐based care (Makic 2025). Central to this role is assisting individuals to make more informed health‐related decisions by evaluating and improving their health literacy (McCaskill et al. 2024). Therefore, nurses’ professional health literacy, defined as the ability to seek, understand, and evaluate health information, focuses on the health literacy of their profession. In this context, nurses’ professional health literacy is a critical protective factor. Integrating digital literacy into lifelong learning for healthcare professionals to counter misinformation effectively is crucial to combat online health misinformation. Interventions such as educational programs, psychological inoculation, and source credibility labeling have shown promise, though evidence on their long‐term effectiveness remains mixed (Grover et al. 2025; Wamala Andersson and Gonzalez 2025).

However, the existing literature on measuring online health misinformation among nurses remains notably scarce. Although health misinformation is increasingly recognized as a critical challenge within healthcare education, there is a clear lack of empirical studies that evaluate susceptibility in nurses. Therefore, the aim of our study was to examine factors associated with online health misinformation in nurses. In particular, we examined the association between several demographic variables, nurses’ perceived integrity of scientists, and online health misinformation susceptibility.

Given our focus on the association between nurses’ perceptions of scientists’ integrity, educational attainment, political interest, and susceptibility to online health misinformation, we further propose a conceptual framework to elucidate the potential mechanisms underlying these associations. In particular, models focused on cognitive ability and knowledge suggest that people with higher education levels have better analytical thinking and scientific understanding, which helps them assess evidence more accurately and spot false or misleading information, thus lowering their risk of being misled (American Psychiatric Association 2023; Office of the U.S. Surgeon General 2021). Likewise, theories about trust and credibility emphasize that having confidence in scientists influences people to accept information that aligns with solid scientific evidence, meaning that greater trust in scientists acts as a filter that reduces dependence on unverified online sources (American Psychiatric Association 2023; Khullar 2022; Viswanath et al. 2025). At the same time, motivated reasoning and identity‐protective cognition theories clarify the role of political interest, showing that politically engaged individuals tend to interpret information in ways that support their existing beliefs, making them more vulnerable to misinformation that matches their identity‐based views (American Psychiatric Association 2023; Office of the U.S. Surgeon General 2021). In this context, the elaboration likelihood model could offer a broad explanation by proposing that those with more education and stronger trust in experts are more likely to process information carefully and thoughtfully, while people with low trust or limited cognitive engagement tend to rely on shortcuts and emotionally charged content, which are common features of online misinformation (Petty and Briñol 2012).

2. Methods

2.1. Study Design

A cross‐sectional study was conducted in Greece, with data collection taking place via an online survey in October 2025. The study questionnaire was developed using Google Forms and disseminated through Facebook and Instagram groups targeting nurses. Additionally, we distributed the survey by sending direct messages to nurses on LinkedIn. This approach yielded a convenience sample. Eligible participants had to meet the following criteria: (1) be employed as clinical nurses in healthcare settings, (2) spend at least 30 minutes per day on the web or social media, and (3) provide informed consent to participate. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Von Elm et al. 2008).

We used G*Power version 3.1.9.2 to estimate the required sample size. An a priori power analysis was performed using the test “Linear multiple regression: Fixed model, single regression coefficient.” Assuming a small‐to‐moderate effect size (Cohen's f 2 = 0.04), a two‐sided significance level of 0.05 (α = 0.05), a statistical power of 95% (1 − β = 0.95), and eight predictors, the minimum required sample size was estimated at 327 nurses.

2.2. Measurements

We measured several demographic characteristics of nurses: gender (males or females), age (continuous variable), MSc/PhD diploma (no or yes), financial status (self‐assessment scale from 0 [very poor financial status] to 10 [excellent financial status]), level of trust in websites to report the news with integrity (self‐assessment scale from 0 [not at all] to 10 [completely]), level of interest in politics (self‐assessment scale from 0 [not at all] to 10 [completely]), and daily time in web/social media (continuous variable in hours).

We used the Health‐Related Online Misinformation Susceptibility Scale (HR‐OMISS) (Katsiroumpa et al. 2026) to measure online health misinformation susceptibility in our nurses. The HR‐OMISS is an adapted version of the OMISS (Katsiroumpa et al. 2025) that measures online misinformation susceptibility in general, while the HR‐OMISS measures specifically online health misinformation susceptibility. We should notice that the HR‐OMISS does not directly assess whether participants believe, accept, or share online health misinformation. The HR‐OMISS is a self‐reported scale that measures behavioral susceptibility to online health misinformation. In other words, the HR‐OMISS measures individuals’ lower verification behavior toward online health misinformation. The HR‐OMISS includes nine items, such as “When you see a health‐related post or story that interests you on social media or websites, how often do you check the website domain and URL?” and “When you see a health‐related post or story that interests you on social media or websites, how often do you check the publication date of the post?” Answers are on a five‐point Likert scale: never (5), rarely (4), sometimes (3), very often (2), always (1). Total score ranges from 9 to 45. Higher scores indicate higher misinformation behavioral susceptibility. In particular, higher scores indicate behavioral vulnerability since the HR‐OMISS measures the frequency of behaviors such as how often healthcare professionals seek more information about the author of the health‐related post, search other reliable sources on the web, etc. Developers of the OMISS suggest a cutoff point (≥23) (Katsiroumpa, Moisoglou, Gallos, et al. 2025) to distinguish individuals who show high levels of misinformation behavioral susceptibility from those who show normal levels of misinformation behavioral susceptibility. Similarly, the proposed cutoff point for the HR‐OMISS is 23 (Katsiroumpa et al. 2026). Thus, this cutoff point is suggested both for healthcare professionals (Katsiroumpa et al. 2026) and for other workers (Katsiroumpa, Moisoglou, Gallos, et al. 2025). In particular, developers of the HR‐OMISS used two external criteria (i.e., the Conspiracy Mentality Questionnaire and the Trust in Scientists Scale) to assess the best cutoff point of the scale. In that case, the receiver operating characteristic (ROC) analysis was used to identify the sensitivity and specificity of the HR‐OMISS. ROC analysis using the Conspiracy Mentality Questionnaire (Bruder et al. 2013) found that the best cutoff point for the HR‐OMISS is 23, with a sensitivity of 0.676, a specificity of 0.525, and area under the curve of 0.610 (95% confidence interval: 0.527 to 0.694, p < 0.001). Similarly, ROC analysis using the Trust in Scientists Scale (Cologna et al. 2025) found the same best cutoff point (i.e., 23) with a sensitivity of 0.736, a specificity of 0.573, and area under the curve of 0.696 (95% confidence interval: 0.628 to 0.763, p < 0.001). We should emphasize that the ≥23 threshold should be considered as a proposed or preliminary threshold, rather than a definitive validated classification of susceptibility, since the evidence on this issue is extremely limited. We used the valid Greek version of the HR‐OMISS (Katsiroumpa et al. 2026). In our study, the Cronbach's alpha for the HR‐OMISS was 0.929. Developers of the HR‐OMISS also found high internal reliability of the scale since Cronbach's alpha was 0.920 and McDonald's Omega was 0.922. Additionally, they performed a test–retest study identifying high reliability between the two measurements since the intraclass correlation coefficient for the HR‐OMISS was 0.986. Confirmatory factor analysis supported the one‐factor model for the HR‐OMISS, such as the original scale, i.e., the OMISS. Moreover, the HR‐OMISS showed very good concurrent validity since the developers of the scale found significant correlations between HR‐OMISS, the Conspiracy Mentality Questionnaire, and the single‐item scientists’ confidence scale (Katsiroumpa et al. 2026).

To assess nurses’ perceived integrity of scientists, we employed the Trust in Scientists Scale (TISS) (Cologna et al. 2025), which consists of 12 items designed to measure four key dimensions: integrity, competence, benevolence, and openness. Given the high correlation among these dimensions, we selected integrity as a representative factor for use in our study. The integrity scale includes three items: How honest or dishonest are most scientists? How ethical or unethical are most scientists? and How sincere or insincere are most scientists? Thus, in our study, we measured nurses’ perceived integrity of scientists. Responses were recorded on a five‐point Likert scale, ranging from 1 (very dishonest/unethical/insincere) to 5 (very honest/ethical/sincere). The integrity score was computed as the mean of the three item scores, resulting in a total score ranging from 1 to 5. Higher scores reflect greater levels of nurses’ perceived integrity of scientists. We used the validated Greek version of the TISS (Cologna et al. 2025). In our study, Cronbach's alpha for the integrity scale was 0.913.

2.3. Ethical Issues

We conducted our study in accordance with the Declaration of Helsinki (“World Medical Association Declaration of Helsinki,” 2013). Moreover, the Ethics Committee of the Faculty of Nursing, National and Kapodistrian University of Athens approved our study protocol (approval No. 75, July 13, 2025). We collected our data on an anonymous and voluntary basis. We informed nurses about the aim and the design of our study, and they gave their informed consent.

2.4. Statistical Analysis

We present categorical variables as numbers and percentages. Also, we used mean, standard deviation (SD), median, minimum value, and maximum value to present continuous variables. We used the Kolmogorov–Smirnov test and Q–Q plots to examine the distribution of continuous variables. We found that continuous variables followed a normal distribution. Demographic variables and nurses’ perceived integrity of scientists were the independent variables, while online health misinformation susceptibility was the dependent variable. Since our dependent variable was a continuous variable that followed a normal distribution, we employed linear regression analysis. First, we performed simple linear regression analysis, and then we constructed a final multivariable model to estimate the independent effect of each independent variable on the outcome. We present unadjusted and adjusted unstandardized B coefficients, 95% confidence intervals (CI), and p‐values. Multicollinearity was assessed through the calculation of variance inflation factors (VIFs), with values exceeding four considered indicative of potential collinearity issues. To evaluate multivariable normality, we inspected histograms of the residuals. Additionally, scatterplots of residuals against predicted values were reviewed to assess homoscedasticity and linearity. We calculated Pearson's correlation coefficient to examine correlations between the study scales since scores on these scales followed a normal distribution. P‐values less than 0.05 were considered statistically significant. We used IBM SPSS 28.0 (IBM Corp., released 2021; IBM SPSS Statistics for Windows, Version 28.0, Armonk, NY: IBM Corp) for the analysis.

3. Results

3.1. Demographic Characteristics

Table 1 shows demographic characteristics of the study sample. Our study sample included 373 nurses. Among them, 81.5% were females, and 72.7% held an MSc/PhD diploma. Mean age was 41.98 years (SD = 10.38) with a median of 43 years, a minimum age of 24 years, and a maximum age of 67 years. The mean score of financial status was 5.96 (SD = 1.46), while the median score was 6 (range = 0 to 10). The mean trust score in websites was 3.69 (SD = 1.97), with a median of 4 and a range between 0 and 9. The mean score of interest in politics was 5.23 (SD = 2.75), while the median score was 6 (range = 0 to 10). Mean daily time on the web/social media was 2.94 hours (SD = 2.13), with a median of 2.5 hours, a minimum of 30 minutes, and a maximum of 8 hours.

TABLE 1.

Demographic characteristics of the study sample (N = 373).

Characteristics N %
Gender
Males 69 18.5
Females 304 81.5
Age a 41.98 10.38
MSc/PhD diploma
No 102 27.3
Yes 271 72.7
Financial status a 5.96 1.46
Trust in websites a 3.69 1.97
Interest in politics a 5.23 2.75
Daily time on web/social media (hours) a 2.94 2.13
Open in a new tab
a

Mean, standard deviation.

3.2. Study Scales

Table 2 presents descriptive statistics for the study scales. The mean score on HR‐OMISS was 26.29 (SD = 8.89), with a median of 26. Also, 63.0% (n = 235) of our nurses met the proposed cutoff score. The mean score of nurses’ perceived integrity of scientists was 3.25 (SD = 0.66).

TABLE 2.

Descriptive statistics for the study scales (N = 373).

Scale Mean SD Median Minimum value Maximum value
Health‐Related Online Misinformation Susceptibility Scale 26.29 8.89 26 9 45
Nurses’ Perceived Integrity of Scientists 3.25 0.66 3 1 5
Open in a new tab

We found a negative correlation between online misinformation susceptibility and nurses’ perceived integrity of scientists (r = −0.320, p < 0.001).

3.3. Dependent Variable: Online Health Misinformation Susceptibility

Table 3 shows linear regression models with HR‐OMISS as the dependent variable. We found that nurses’ perceived integrity of scientists is associated with lower online health misinformation behavioral susceptibility (adjusted unstandardized B coefficient = −3.290, 95% CI = −4.687 to −1.893, p < 0.001). Moreover, we found that nurses with an MSc/PhD diploma had lower levels of misinformation behavioral susceptibility (adjusted unstandardized B coefficient = −2.470, 95% CI = −4.428 to −0.512, p = 0.014). Additionally, interest in politics was associated with lower misinformation behavioral susceptibility (adjusted unstandardized B coefficient = −0.794, 95% CI = −1.119 to −0.469, p < 0.001). The VIF values obtained from the final model indicated that multicollinearity was not a concern since values ranged from 1.073 to 1.236. Supplementary Figure 1 illustrates that the assumption of multivariate normality is satisfied, as the residuals follow a normal distribution. Furthermore, Supplementary Figure 2 confirms that the assumptions of homoscedasticity and linearity are upheld for the multivariable model.

TABLE 3.

Linear regression models with the Health‐Related OMISS as the dependent variable (N = 373).

Univariate models Multivariable model a
Independent variables Unadjusted unstandardized B coefficient 95% CI for B P‐value Adjusted unstandardized B coefficient 95% CI for B P‐value Variance inflation factor
Females vs. males 2.101 −0.224 to 4.426 0.076 0.779 −1.428 to 2.986 0.488 1.073
Age −0.034 −0.121 to 0.053 0.444 −0.028 −0.112 to 0.056 0.513 1.103
MSc/PhD diploma −4.240 −6.227 to −2.252 <0.001 −2.470 −4.428 to −0.512 0.014 1.113
Financial status −0.365 −0.984 to 0.254 0.247 0.285 −0.329 to 0.900 0.362 1.178
Trust in websites −0.231 −0.690 to 0.228 0.323 0.128 −0.324 to 0.580 0.579 1.159
Interest in politics −1.053 −1.365 to −0.740 <0.001 −0.794 −1.119 to −0.469 <0.001 1.161
Daily time on web/social media 0.200 −0.226 to 0.625 0.357 0.026 −0.382 to 0.435 0.899 1.105
Nurses’ perceived integrity of scientists −4.311 −5.615 to −3.006 <0.001 −3.290 −4.687 to −1.893 <0.001 1.236
Open in a new tab
a

R 2 for the multivariable model = 16.5%, p‐value for ANOVA < 0.001.

CI: confidence interval.

4. Discussion

Identifying the factors associated with nurses’ susceptibility to online health misinformation is of paramount importance, given their critical role as frontline healthcare professionals responsible for promoting health, delivering evidence‐based care, and guiding individuals toward informed health‐related decisions. As trusted sources of information, nurses significantly shape patient understanding and adherence to accurate health practices; therefore, recognizing and addressing factors associated with misinformation vulnerability are essential to safeguard public health and maintain confidence in healthcare systems.

In this context, the present study was designed to evaluate, for the first time, the level of online health misinformation behavioral susceptibility among nurses and to explore factors associated with its occurrence. Multivariable analysis showed that nurses’ perceived integrity of scientists, educational level, and interest in politics are associated with online health misinformation behavioral susceptibility in nurses. Our final multivariable model explained 16.5% of the variance in online health misinformation susceptibility. Therefore, our study showed that substantial unexplained variance remains beyond the independent variables that we examined. Also, this finding highlights the multifactorial nature of online health misinformation behavioral susceptibility. Thus, there is a need to further examine potential factors that could be associated with misinformation susceptibility among nurses.

The finding that 63.0% of our convenience sample met the proposed cutoff point suggests that misinformation is not merely a public issue but could also affect healthcare professionals who are expected to serve as reliable sources of evidence‐based information (Makic 2025). First, the increasing reliance on digital platforms for professional updates and patient education exposes nurses to vast amounts of unverified content, often amplified by social media algorithms that prioritize engagement over accuracy. Second, gaps in digital health literacy and limited training in critical appraisal skills may hinder nurses’ ability to differentiate credible sources from misleading information. However, we should recognize that the ≥23 threshold is a preliminary threshold and, thus, the finding that 63.0% of our nurses met the threshold should be considered an exploratory sample‐specific proportion rather than a prevalence estimate of nurses in Greece. Further studies should be conducted to expand our knowledge on this issue and identify more valid cutoff points for the HR‐OMISS in different populations, countries, and clinical settings.

Our findings indicated that nurses’ perceived integrity of scientists is associated with lower nurses’ behavioral susceptibility to online health misinformation. This result aligns with prior research emphasizing the role of institutional trust as a protective factor against misinformation (Bhattacharya and Singh 2025; Scherer and Pennycook 2020). Trust in science is a literature‐based construct and functions as a cognitive anchor, guiding individuals toward evidence‐based sources and away from unverified claims. Nurses who exhibit high trust in scientific processes are more likely to engage in critical appraisal, seek corroboration from peer‐reviewed literature, and reject anecdotal or pseudoscientific narratives (Wamala Andersson and Gonzalez 2025). From a psychological perspective, trust in science enhances epistemic vigilance, reducing reliance on heuristics and mitigating biases such as the illusory truth effect and motivated reasoning, which often lead individuals to accept repeated or belief‐congruent misinformation (Bhattacharya and Singh 2025). Conversely, low trust in science creates an environment where misinformation thrives, as individuals may perceive scientific recommendations as biased or politically motivated, making alternative narratives more appealing (Deori et al. 2025).

Our study found that higher educational attainment is associated with lower behavioral susceptibility to online health misinformation. This finding is consistent with prior research indicating that education enhances critical thinking skills, scientific reasoning, and the ability to evaluate evidence‐based sources (Alotaibi et al. 2025; Scherer and Pennycook 2020). Education provides nurses with a stronger foundation in health sciences, enabling them to recognize inaccuracies and apply professional judgment when encountering questionable information online (Sharman 2023). From a cognitive perspective, higher education fosters analytical processing rather than heuristic shortcuts, reducing reliance on superficial cues such as source familiarity or emotional appeal. It also mitigates the illusory truth effect, as educated individuals are more likely to question repeated claims and seek corroboration from credible sources (Bhattacharya and Singh 2025). Furthermore, advanced education often includes exposure to research methodology and evidence‐based practice, which strengthens epistemic vigilance and skepticism toward unverified content.

This study revealed that greater interest in politics is associated with lower behavioral susceptibility to health misinformation among nurses. This finding was contrary to our initial theoretical expectation, based on motivated reasoning and identity‐protective cognition that politically engaged individuals might be more susceptible to misinformation when it is consistent with their pre‐existing beliefs or social identities. One possible explanation is that political interest, although it may increase exposure to identity‐congruent claims, may also be associated with broader information exposure and greater familiarity with evaluating competing arguments and information sources (Scherer and Pennycook 2020). These behaviors foster analytical thinking and skepticism toward unverified content, which can extend beyond political topics to health‐related information (Bhattacharya and Singh 2025). Political engagement often correlates with higher media literacy, as politically interested individuals are more accustomed to identifying bias, verifying sources, and questioning narratives (Deori et al. 2025). This vigilance can reduce reliance on heuristics and mitigate cognitive biases such as the illusory truth effect, which makes repeated misinformation seem credible. Furthermore, political interest may reflect a broader orientation toward civic responsibility and trust in institutional processes, including science and evidence‐based policy, which reinforces resistance to misinformation (Wamala Andersson and Gonzalez 2025). However, these explanations remain hypotheses because media literacy, broader information exposure, and civic engagement were not directly measured in the present study. Moreover, political interest should not necessarily be equated with ideological commitment or political polarization, which may be more closely related to motivated reasoning. Given the cross‐sectional design, the direction of the association cannot be established, and further research should examine whether media literacy, information‐source diversity, and civic engagement mediate or moderate the association between political interest and behavioral susceptibility to health misinformation.

Several limitations should be considered when interpreting this study's findings. First, the sample consisted solely of nurses, which may restrict the applicability of results to other healthcare fields from diverse cultural and educational backgrounds. Future studies using random, representative samples across different countries and healthcare professionals would provide more robust insights. Second, although a validated scale was employed to assess misinformation behavioral susceptibility, the study did not account for differences in platform algorithms or content exposure, which could shape individual experiences. Third, the use of a cross‐sectional design limits the ability to draw causal conclusions between demographic characteristics, nurses’ perceived integrity of science, and vulnerability to online health misinformation. Longitudinal research is needed to clarify temporal patterns and causality. Fourth, reliance on self‐reported data introduces potential social desirability bias and inaccuracies in reporting behaviors such as time spent on social media. Moreover, the focus of this study was on demographic factors, leaving out other relevant variables such as cognitive styles, digital literacy, and psychological traits. Future research should incorporate these dimensions for a more comprehensive understanding of misinformation vulnerability. Fifth, the HR‐OMISS is a self‐reported instrument that assesses behavioral susceptibility to online health misinformation through individuals’ reported information‐verification practices. Consequently, although the scale provides valuable insight into behaviors associated with vulnerability to misinformation, it does not directly evaluate whether nurses believe, accept, or disseminate health misinformation encountered online. As with all self‐reported measures, the possibility of information bias, including social desirability and reporting bias, cannot be excluded. Therefore, the findings should be interpreted with caution. Future research could build upon the present study by incorporating more direct measures of misinformation endorsement and sharing behaviors, thereby providing a more comprehensive assessment of nurses’ susceptibility to online health misinformation. Sixth, the proposed HR‐OMISS cutoff score was derived in an initial validation study, and, thus, the classification of nurses into categories of higher and lower susceptibility is inherently a simplification of a complex and continuous construct. Dichotomization of continuous scores may result in some loss of information and may not fully capture the spectrum of online health misinformation behavioral susceptibility. Furthermore, the proportion of nurses exceeding the cutoff should be interpreted with caution, as our sample was obtained through convenience sampling and is not necessarily representative of all nurses in Greece. Moreover, only one study has suggested a cutoff point for the HR‐OMISS, with modest discrimination and relatively limited specificity. Therefore, the finding that 63.0% of participants met the proposed threshold reflects only the characteristics of the present study sample and should not be interpreted as a prevalence estimate for the broader nursing population. In other words, the finding that 63% of our convenience sample met the suggested ≥23 threshold should be considered as an exploratory sample‐specific finding rather than a prevalence estimate. Future studies employing representative sampling strategies and external behavioral measures of misinformation susceptibility could further evaluate the applicability and utility of this threshold among nurses. Seventh, the study sample was recruited through social media platforms and professional networking sites using a convenience sampling approach. Consequently, participation was self‐selected, which may have introduced selection bias, as nurses with greater interest in online information, digital technologies, or professional development may have been more likely to participate. In addition, a large proportion of participants held a postgraduate qualification (MSc/PhD), which may not reflect the educational profile of the broader nursing workforce in Greece. Therefore, caution is warranted when interpreting the findings, as the sample may not be representative of all Greek nurses. In particular, the proportion of participants who exceeded the proposed HR‐OMISS cutoff should not be interpreted as a population prevalence estimate but rather as a finding specific to this convenience sample. Future studies employing probability‐based sampling methods and more representative samples are needed to enhance the generalizability of the results. Finally, recruitment of nurses was conducted through social media platforms, which introduces the possibility of selection bias. In particular, nurses with greater digital engagement or stronger preexisting opinions may have been more inclined to participate. The impact of participation by digitally engaged nurses on our findings is difficult to determine. On the one hand, higher digital engagement may be associated with increased digital literacy, potentially resulting in lower susceptibility to online health misinformation. Conversely, elevated levels of digital engagement may expose nurses to greater volumes of unverified content, thereby increasing misinformation susceptibility among individuals who routinely scroll through social media without verifying the accuracy of the information encountered. Furthermore, we established a minimum threshold of 30 minutes of daily internet or social media use as an inclusion criterion to ensure a basic level of exposure to online health misinformation among nurses.

5. Conclusions

This study was undertaken to measure health misinformation behavioral susceptibility among nurses and to identify factors associated with its occurrence. By systematically measuring misinformation levels and examining factors that could be associated with misinformation, we aim to provide evidence that can inform targeted interventions, enhance digital health literacy, and strengthen nurses’ capacity to deliver accurate, evidence‐based information in clinical and public health contexts.

5.1. Implications for Nursing Practice and Health Policy

We found that higher levels of nurses’ perceived integrity of scientists, higher educational level, and higher interest in politics were independently associated with lower levels of online health misinformation behavioral susceptibility. Given the widespread use of digital information sources by healthcare professionals, further attention to factors associated with misinformation susceptibility is warranted. Therefore, initiatives aimed at strengthening critical evaluation of online health information and fostering engagement with evidence‐based scientific sources may merit consideration within nursing education and professional development programs.

Educational approaches that enhance digital health literacy, media literacy, and critical appraisal skills among nurses may improve their ability to handle online health misinformation. Such approaches may include training on evaluating source credibility, identifying misinformation indicators, assessing the quality of online health information, and using evidence‐based databases and verified professional resources. Healthcare organizations may also consider facilitating access to reliable scientific information and evidence‐based educational materials.

The present study did not evaluate the effectiveness of specific educational, organizational, or policy interventions. In addition, it did not assess misinformation sharing behaviors, patient outcomes, or harms associated with misinformation exposure. Therefore, future research should investigate whether digital health literacy programs, misinformation‐detection training, curricular interventions, or organizational policies can effectively reduce misinformation susceptibility among nurses and improve professional and patient‐related outcomes.

Author Contributions

Study design: AK, IM, OK, and PG. Data collection: AK, IM, OK, and PG. Data analysis: AK, OK, and PG. Study supervision: PG. Manuscript writing: AK, IM, OK, and PG.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Supporting information

Supplementary Figure 1: Histogram of the residuals with Health‐related Online Misinformation Susceptibility Scale as the dependent variable.

INR-73-0-s002.tiff (36.9KB, tiff)

Supplementary Figure 2: Scatterplot of residuals versus predicted values with Health‐related Online Misinformation Susceptibility Scale as the dependent variable.

INR-73-0-s001.jpg (37.7KB, jpg)

Acknowledgments

The author has nothing to report.

The publication of this article in OA mode was financially supported by HEAL‐Link.

Data Availability Statement

The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.30661352.

References

  1. Alotaibi, N. , Wilson C. B., and Traynor M.. 2025. “Enhancing Digital Readiness and Capability in Healthcare: A Systematic Review of Interventions, Barriers, and Facilitators.” BMC Health Services Research 25, no. 1: 500. 10.1186/s12913-025-12663-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Psychiatric Association . 2023. “Using Psychological Science to Understand and Fight Health Misinformation.” An APA Consensus Statement APA. 1–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arora, V. M. , Madison S., and Simpson L.. 2020. “Addressing Medical Misinformation in the Patient–Clinician Relationship.” Jama 324, no. 23: 2367. 10.1001/jama.2020.4263. [DOI] [PubMed] [Google Scholar]
  4. Bail, C. A. , Argyle L. P., Brown T. W., et al. 2018. “Exposure to Opposing Views on Social Media Can Increase Political Polarization.” Proceedings of the National Academy of Sciences 115, no. 37: 9216–9221. 10.1073/pnas.1804840115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Baronchelli, A. 2018. “The Emergence of Consensus: A Primer.” Royal Society Open Science 5, no. 2: 172189. 10.1098/rsos.172189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bhattacharya, S. , and Singh A.. 2025. “Unravelling the Infodemic: A Systematic Review of Misinformation Dynamics During the COVID‐19 Pandemic.” Frontiers in Communication 10: 1560936. 10.3389/fcomm.2025.1560936. [DOI] [Google Scholar]
  7. Bruder, M. , Haffke P., Neave N., Nouripanah N., and Imhoff R.. 2013. “Measuring Individual Differences in Generic Beliefs in Conspiracy Theories across Cultures: Conspiracy Mentality Questionnaire.” Frontiers in Psychology 4: 1–15. 10.3389/fpsyg.2013.00225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cinelli, M. , Brugnoli E., Schmidt A. L., Zollo F., Quattrociocchi W., and Scala A.. 2020. “Selective Exposure Shapes the Facebook News Diet.” PLoS ONE 15, no. 3: e0229129. 10.1371/journal.pone.0229129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cinelli, M. , Quattrociocchi W., Galeazzi A., et al. 2020. “The COVID‐19 Social Media Infodemic.” Scientific Reports 10, no. 1: 16598. 10.1038/s41598-020-73510-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cologna, V. , Mede N. G., Berger S., et al. 2025. “Trust in Scientists and Their Role in Society Across 68 Countries.” Nature Human Behaviour 9, no. 4: 713–730. 10.1038/s41562-024-02090-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Del Vicario, M. , Bessi A., Zollo F., et al. 2016. “The Spreading of Misinformation Online.” Proceedings of the National Academy of Sciences 113, no. 3: 554–559. 10.1073/pnas.1517441113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Del Vicario, M. , Vivaldo G., Bessi A., et al. 2016. “Echo Chambers: Emotional Contagion and Group Polarization on Facebook.” Scientific Reports 6, no. 1: 37825. 10.1038/srep37825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Deori, D. , Debbarma P., Talukdar R., Dutta N., Deka M., and Borah R.. 2025. “Impact of Online Misinformation on Mental Well‐Being and Nursing Practice: A Systematic Review.” Vascular and Endovascular Review 8, no. 9: 242–247. [Google Scholar]
  14. Grover, H. , Nour R., Zary N., and Powell L.. 2025. “Online Interventions Addressing Health Misinformation: Scoping Review.” Journal of Medical Internet Research 27: e69618. 10.2196/69618. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Islam, M. S. , Sarkar T., Khan S. H., et al. 2020. “COVID‐19–Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis.” The American Journal of Tropical Medicine and Hygiene 103, no. 4: 1621–1629. 10.4269/ajtmh.20-0812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Katsiroumpa, A. , Konstantakopoulou O., Gallos P., et al. 2026. “Online Misinformation Susceptibility Scale: An Adapted Version for Health‐Related Misinformation.” Archives of Hellenic Medicine 43: 383–389. in press. [Google Scholar]
  17. Katsiroumpa, A. , Moisoglou I., Gallos P., et al. 2025. “Online Misinformation Susceptibility Scale: Determination of an Optimal Cut‐off Point.” International Journal of Caring Sciences 18, no. 3: 1164–1170. [Google Scholar]
  18. Katsiroumpa, A. , Moisoglou I., Mangoulia P., et al. 2025. “The Online Misinformation Susceptibility Scale: Development and Initial Validation.” Healthcare 13, no. 17: 2252. 10.3390/healthcare13172252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kbaier, D. , Kane A., McJury M., and Kenny I.. 2024. “Prevalence of Health Misinformation on Social Media—Challenges and Mitigation Before, During, and Beyond the COVID‐19 Pandemic: Scoping Literature Review.” Journal of Medical Internet Research 26: e38786. 10.2196/38786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Khullar, D. 2022. “Social Media and Medical Misinformation: Confronting New Variants of an Old Problem.” Jama 328, no. 14: 1393. 10.1001/jama.2022.17191. [DOI] [PubMed] [Google Scholar]
  21. Kulshrestha, J. , Eslami M., Messias J., et al. 2017. “Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media.” Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing , 417–432. 10.1145/2998181.2998321. [DOI]
  22. Lee, S. K. , Sun J., Jang S., and Connelly S.. 2022. “Misinformation of COVID‐19 Vaccines and Vaccine Hesitancy.” Scientific Reports 12, no. 1: 13681. 10.1038/s41598-022-17430-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Makic, M. B. F. 2025. “Fostering a Spirit of Inquiry: Inspiring Nurses to Advance Practice Based on Best Evidence.” American Journal of Critical Care 34, no. 4: 255–265. 10.4037/ajcc2025493. [DOI] [PubMed] [Google Scholar]
  24. McCaskill, A. , Gasch‐Gallen A., and Montero‐Marco J.. 2024. “The Effect of Nurse Health Literacy Interventions on Patient Health Literacy Scores in Specialty Consultations: A Quasi‐Experimental Study.” BMC Nursing 23, no. 1: 786. 10.1186/s12912-024-02447-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Office of the U.S. Surgeon General . 2021. Confronting Health Misinformation: The U.S. Surgeon General's Advisory on Building a Healthy Information Environment. Office of the U.S. Surgeon General. [PubMed] [Google Scholar]
  26. Petty, R. E. , and Briñol P.. 2012. “The Elaboration Likelihood Model.” In Handbook of Theories of Social Psychology: Volume 1, edited by P. Van Lange, Kruglanski A. and E. Higgins, 224–245. SAGE Publications Ltd. 10.4135/9781446249215.n12. [DOI] [Google Scholar]
  27. Pierri, F. , Perry B. L., DeVerna M. R., et al. 2022. “Online Misinformation Is Linked to Early COVID‐19 Vaccination Hesitancy and Refusal.” Scientific Reports 12, no. 1: 5966. 10.1038/s41598-022-10070-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Rodrigues, F. , Ziade N., Jatuworapruk K., Caballero‐Uribe C. V., Khursheed T., and Gupta L.. 2023. “The Impact of Social Media on Vaccination: A Narrative Review.” Journal of Korean Medical Science 38, no. 40: e326. 10.3346/jkms.2023.38.e326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Rolls, K. , and Massey D.. 2021. “Social Media Is a Source of Health‐Related Misinformation.” Evidence Based Nursing 24, no. 2: 46–46. 10.1136/ebnurs-2019-103222. [DOI] [PubMed] [Google Scholar]
  30. Scherer, L. D. , and Pennycook G.. 2020. “Who Is Susceptible to Online Health Misinformation?” American Journal of Public Health 110, no. S3: S276–S277. 10.2105/AJPH.2020.305908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Schmidt, A. L. , Zollo F., Del Vicario M., et al. 2017. “Anatomy of News Consumption on Facebook.” Proceedings of the National Academy of Sciences 114, no. 12: 3035–3039. 10.1073/pnas.1617052114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Sharman, J. 2023. “Recognising and Addressing Health Misinformation in Nursing Practice.” Primary Health Care 33, no. 4: 24–29. 10.7748/phc.2023.e1791. [DOI] [Google Scholar]
  33. Starnini, M. , Frasca M., and Baronchelli A.. 2016. “Emergence of Metapopulations and Echo Chambers in Mobile Agents.” Scientific Reports 6, no. 1: 31834. 10.1038/srep31834. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. The Lancet Infectious Diseases . 2020. “The COVID‐19 Infodemic.” The Lancet Infectious Diseases 20, no. 8: 875. 10.1016/S1473-3099(20)30565-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Viswanath K., Taylor T. E., and Rhodes H. G. (Eds.). 2025. Understanding and Addressing Misinformation about Science, 27894. National Academies Press. 10.17226/27894. [DOI] [Google Scholar]
  36. Von Elm, E. , Altman D. G., Egger M., Pocock S. J., Gøtzsche P. C., and 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, no. 4: 344–349. 10.1016/j.jclinepi.2007.11.008. [DOI] [PubMed] [Google Scholar]
  37. Wamala Andersson, S. , and Gonzalez M. P.. 2025. “Digital Health Literacy—A Key Factor in Realizing the Value of Digital Transformation in Healthcare.” Frontiers in Digital Health 7: 1461342. 10.3389/fdgth.2025.1461342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wang, Y. , McKee M., Torbica A., and Stuckler D.. 2019. “Systematic Literature Review on the Spread of Health‐Related Misinformation on Social Media.” Social Science & Medicine 240: 112552. 10.1016/j.socscimed.2019.112552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. World Medical Association Declaration of Helsinki . 2013. “Ethical Principles for Medical Research Involving Human Subjects.” Jama 310, no. 20: 2191. 10.1001/jama.2013.281053. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure 1: Histogram of the residuals with Health‐related Online Misinformation Susceptibility Scale as the dependent variable.

INR-73-0-s002.tiff (36.9KB, tiff)

Supplementary Figure 2: Scatterplot of residuals versus predicted values with Health‐related Online Misinformation Susceptibility Scale as the dependent variable.

INR-73-0-s001.jpg (37.7KB, jpg)

Data Availability Statement

The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.30661352.

Articles from International Nursing Review are provided here courtesy of Wiley

RESOURCES