Background/Objectives: Artificial intelligence (AI)-based chatbots present a viable approach to overcoming several challenges associated with conventional psychotherapy, such as high financial costs, limited access to mental health professionals, and geographical or logistical barriers. Thus, these chatbots are increasingly employed as complementary tools to traditional therapeutic practices in mental health care. Our aim was to develop and validate a scale to measure attitudes toward the use of AI-based chatbots for mental health support, i.e., the Artificial Intelligence in Mental Health Scale (AIMHS). Methods: A multidisciplinary panel of experts assessed the content validity. To confirm face validity, we carried out cognitive interviews and calculated the item-level face validity index. We applied factor analysis to verify the construct structure. We assessed measurement invariance across demographic subgroups. Concurrent validity was evaluated using three valid instruments. Reliability was tested through Cronbach’s alpha, Cohen’s kappa, and the intraclass correlation coefficient. Results: Factor analysis supported a two-factor five-item model. The two factors were technical and personal advantages, and explained 81.28% of the variance. The AIMHS demonstrated adequate concurrent validity, evidenced by statistically significant correlations with Artificial Intelligence Attitude Scale (r = 0.405, p-value < 0.001), Attitudes Towards Artificial Intelligence Scale (acceptance subscale; r = 0.401, p-value < 0.001, fear subscale; r = −0.151, p-value = 0.002), and Short Trust in Automation Scale (r = 0.450, p-value < 0.001). Configural, metric and scalar invariance were supported by our findings. Cronbach’s alpha was 0.798, and intraclass correlation coefficient was 0.938. Cohen’s kappa for the five items ranged from 0.760 to 0.848. Conclusions: The AIMHS is a five-item psychometrically sound and user-friendly instrument capturing two dimensions; technical and personal advantages. Future research should be undertaken to further evaluate the psychometric properties of the AIMHS across diverse populations and contexts.

Background: Health-related misinformation is a pervasive phenomenon that expanded substantially during the COVID-19 pandemic, particularly regarding concerns about vaccine safety and effectiveness. Objective: The aim of this study was to investigate the impact of online health misinformation susceptibility on health behaviors and vaccine hesitancy. Methods: A cross-sectional study with a convenience sample of 402 individuals was conducted in Greece, with data collected via an online survey during September 2025. We used the Health-Related Online Misinformation Susceptibility Scale to measure online health misinformation susceptibility. The Health Behavior Inventory–Short Form was used to measure health behaviors, while the Vaccine Hesitancy Scale (VHS) was used to measure participants’ hesitancy towards vaccination. We performed multivariable analysis to identify the independent effect of health misinformation after adjustment for several confounders. Results: Multivariable linear regression analysis showed a positive association between online health misinformation susceptibility and diet score (adjusted coefficient beta = 0.026; 95% confidence interval [CI] = 0.006 to 0.046; p = 0.010) and anger and stress score (adjusted coefficient beta = 0.033; 95% CI = 0.013 to 0.052; p = 0.001). After adjustment for confounders, we found a positive association between online health misinformation susceptibility and score on the factors “lack of confidence” (adjusted coefficient beta = 0.016; 95% CI = 0.005 to 0.028; p = 0.006) and “risk perception” (adjusted coefficient beta = 0.023; 95% CI = 0.010 to 0.036; p = 0.001). Conclusions: Our findings suggest that higher susceptibility to online health misinformation is associated with poorer health behaviors and greater vaccine hesitancy.

Background: Online misinformation has grown significantly with the widespread use of the internet and the ease of sharing content through social media, often without rigorous fact-checking or scientific validation. In this context, trust in scientists and conspiracy beliefs may affect online misinformation susceptibility and fake news detection. Objective: To examine the effect of trust in scientists and conspiracy beliefs on online misinformation susceptibility and fake news detection. Additionally, we investigated the association between demographic variables and online misinformation susceptibility and fake news detection. Methods: A cross-sectional study was carried out in Greece, with data collected through an online survey in August 2025. Trust in scientists was assessed using the Trust in Scientists Scale. Conspiracy beliefs were measured using the Conspiracy Mentality Questionnaire. Participants’ susceptibility to online misinformation was evaluated using the Online Misinformation Susceptibility Scale. Their ability to detect online fake news was assessed through the fake news detection scale. Results: Our multivariable analysis identified that lower trust in scientists is associated with higher online misinformation susceptibility. Participants who believed in conspiracy behaviors showed also higher levels of misinformation susceptibility. Moreover, lower financial status and lower interest in politics were associated with misinformation susceptibility. We found a positive association between trust in websites and misinformation susceptibility. Also, we found a negative association between age and misinformation susceptibility. Our multivariable linear regression model showed that participants who believe in conspiracy beliefs had a lower ability to detect fake news. Moreover, we found a positive association between trust in sciences and fake news detection. Finally, we found a negative association between interest in politics and fake news detection. Conclusion: Our findings showed associations between trust in scientists, conspiracy beliefs, online misinformation susceptibility and fake news detection. Moreover, several demographic variables were associated with misinformation susceptibility and fake news detection. Identification of predictors of misinformation susceptibility and fake news detection is crucial to define high-risk groups and develop appropriate interventions to confront these issues.

Background: Health-related misinformation is a pervasive phenomenon that expanded substantially during the COVID-19 pandemic, particularly regarding concerns about vaccine safety and effectiveness. Objective: To investigate the impact of online health misinformation susceptibility on health behaviors and vaccine hesitancy. Methods: A cross-sectional study was conducted in Greece, with data collected via an online survey during September 2025. We used the Health-Related Online Misinformation Susceptibility Scale to measure online health misinformation susceptibility. Health Behavior Inventory – Short Form was used to measure health behaviors, while Vaccine Hesitancy Scale (VHS) was used to measure participants’ hesitancy towards vaccination. We performed multivariable analysis to identify the independent effect of health misinformation after adjustment for several confounders. Results: Multivariable linear regression analysis showed a positive association between online health misinformation susceptibility and diet score (adjusted coefficient beta = 0.026, 95% confidence interval [CI] = 0.006 to 0.046, p = 0.010) and anger and stress score (adjusted coefficient beta = 0.033, 95% CI = 0.013 to 0.052, p = 0.001). After adjustment for confounders, we found a positive association between online health misinformation susceptibility and score on the factors “lack of confidence” (adjusted coefficient beta = 0.016, 95% CI = 0.005 to 0.028, p = 0.006) and “risk perception” (adjusted coefficient beta = 0.023, 95% CI = 0.010 to 0.036, p = 0.001). Conclusion: Our findings suggest that higher susceptibility to online health misinformation is linked to poorer health behaviors and greater vaccine hesitancy.

Objective: To develop and validate an ultra brief scale to measure social media fatigue, i.e., the Social Media Fatigue Scale-3 items (SMFS-3). Method: Construct validity of the SMFS-3 was assessed through corrected item–total correlations and confirmatory factor analysis. Concurrent validity was examined using the Bergen Social Media Addiction Scale (BSMAS), and the Patient Health Questionnaire-4 (PHQ-4). Reliability was evaluated through multiple indices, including Cronbach’s alpha, Cohen’s kappa, and the intraclass correlation coefficient. Receiver Operating Characteristic analysis was employed to determine the optimal cut-off point for the SMFS-3, using the BSMAS as external criterion. Results: Corrected item–total correlations and confirmatory factor analysis confirmed that the final version of the SMFS-3 includes three items in one factor. Concurrent validity of the SMFS-3 was excellent since we found statistically significant correlations between the SMFS-3 and the BSMAS, and the PHQ-4. Cronbach’s alpha for the SMFS-3 was 0.762. Cohen’s kappa for the three items ranged from 0.852 to 0.919 (p < 0.001 in all cases). Additionally, intraclass correlation coefficient was 0.986 (p < 0.001). Thus, the reliability of the SMFS-3 was excellent. The best cut-off point for the SMFS-3 was 10, indicating that social media users with SMFS-3 score ≥10 were considered as users with high levels of social media fatigue, and those with SMFS-3 score <10 as users with normal levels of fatigue. Conclusions: The SMFS-3 is a one-factor 3-item scale with great reliability and validity. The SMFS-3 is a short and easy-to-use tool that measures levels of social media fatigue in a couple of minutes. Valid measurement of social media fatigue with brief and valid tools is essential to further understand predictors and consequences of this fatigue.

Background/Objectives: Artificial intelligence (AI) has transformed healthcare delivery by revolutionizing the offering opportunities in prognosis, diagnosis, personalize treatment, improving patient outcomes. However, little is known about the nurses’ perceptions and attitudes toward the integration of AI-driven conversational technology, AI chatbots into clinical practice. The aim of our study was to investigate nurses’ perceptions regarding the use of AI chatbots as a tool for mental health support. Additionally, the study aimed to evaluate their levels of acceptance and fear toward AI, while examining the influence of demographic variables on these attitudes. Methods: A cross-sectional study was conducted. Attitudes toward the use of AI-powered chatbots for mental health support were measured using the Artificial Intelligence in Mental Health Scale (AIMHS). Additionally, the Attitudes Towards Artificial Intelligence Scale (ATAI) was employed to assess nurses’ levels of acceptance and fear regarding artificial intelligence. Results: AIMHS score reflected moderately positive attitudes toward AI chatbots for mental health support, while ATAI scores indicated a moderate level of acceptance and fear toward AI. Multivariable analysis showed that increased age and increased daily engagement with social media and websites were significantly associated with more favorable technical perceptions of AI-based mental health chatbots. Also, male nurses exhibited significantly more favorable attitudes toward AI-based mental health chatbots in terms of perceived personal benefits. Higher levels of digital technology competence were significantly associated with greater acceptance of artificial intelligence. Additionally, male nurses reported significantly higher acceptance of AI compared to their female counterparts. We found that lower financial status was significantly associated with heightened fear of AI. Conclusions: Nurses generally held moderately positive attitudes toward both AI-based mental health chatbots and AI more broadly. Several demographic factors were found to significantly influence these attitudes.