(1) Background: evidence-based nursing has been widely adopted by healthcare facilitators, and it is predicated on the connection between research evidence and clinical practice. The knowledge and implementation of evidence-based practice (EBP) depend on a variety of long-established barriers and attitudes. The purpose of this study was to translate and validate the Greek version of the evidence-based practice competence questionnaire (EBP-COQ) and test it on a sample of Greek undergraduate nursing students. (2) Methods: a cross-sectional analysis was conducted on data obtained between November 2018 and January 2019. A convenience sample of 320 Greek undergraduate nursing students participated in a survey to examine the psychometric properties of the tool. The reliability and validity of the tool was examined. Cronbach’s coefficient alpha was used to determine the scale’s internal consistency reliability. (3) Results: the EBP-COQ was translated and validated. The estimated Cronbach’s alpha was higher than 0.70 for all scales. Of the participants, 22.5% were men and 77.5% were women, while 31% of them were in their final year of nursing education. A total of 61% of the students stated that they had not participated in an EBP seminar in the past. High Spearman’s correlation coefficients were found for “Knowledge in EBP” with “Personal attitude towards EBP” (rs = 0.329, p < 0.001). (4) Conclusions: the Greek version of the EBP-COQ is a valid instrument that can be used in the Greek population. It provides information about attitude, knowledge and skills in the EBP approach.

(1) Background: The current uses of smartwatch wearable devices have expanded, not only being a part of everyday routine life but also playing a dynamic role in the early detection of many behavioral patterns of users. Furthermore, in the modern era, there is an increasing trend of mental disturbances even in early adolescence, a phenomenon that continues into academic life. Taking into account the current situation, the objective of this systematic literature review emphasizes the role of AI wearable devices in the early symptom detection of burnout in the student population. (2) Methods: A systematic literature review was designed based on the PRISMA guidelines. The general extracted aspect was to exploit all the current related research evidence about the effectiveness of wearable devices in the student population. (3) Results: The reviewed studies document the importance of physiological monitoring and AI-driven predictive models, with the collaboration of self-reported scales in assessing mental well-being. It is reported that stress is the most frequently studied burnout-related symptom. Meanwhile, heart rate (HR) and heart rate variability (HRV) are the most commonly used biomarkers that can be used to monitor and evaluate early burnout detection. (4) Conclusions: Despite the promising potential of these technologies, several challenges and limitations must be addressed to enhance their effectiveness and reliability.

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.

Online bibliographic databases constitute the access to information supporting evidence-based practice. The purpose of the present survey is to evaluate the awareness, the use and the impact that online evidences have in clinical practice. An online survey was conducted using a 25 item questionnaire which was completed by 263 healthcare practitioners (nurses and doctors). Chi-square analysis and t-tests were used and comparisons between both groups were made. Multiple logistic regression to predict databases use was employed. Ninety-three percent (n = 206) of participants had heard about the online bibliographic databases; 5.7% of participant sample have heard about online databases but they do not use them. 18.3% was the total sample of non-users (n = 48). The main reason of using was filling a knowledge gap (50.7%), and the main reasons of not using were both that they use another information source and they don't need them to do their job. Both nurses and doctors believe that the use of online databases improves patient care it is a part of their legitimate clinical role. They need more training to improve their searching skills, enhancing them in everyday clinical place.

Across-sectional survey was designed to determine health professionals' awareness and usage of online evidence retrieval systems in clinical practice. A questionnaire was used to measure professionals' behavior and utilization of online evidences, as well as, reasons and barriers on information retrieval. 439 nurses and physicians from public and private hospitals in Greece formulate the study's sample. The two most common reasons that individuals are using online information systems were for writing scientific manuscripts or filling a knowledge gap. A positive correlation was found between participants with postgraduate studies and information system usage. The majority of them (90,6%) believe that online information systems improves patient care and 67,6% of them had their own experiences on this. More support is needed to nurses and physicians in order to use the online evidence and as a result to improve the provided care and practices.

(1) Background: Current uses of smartwatches wearable devices have been expanded not only in everyday routine life but also, they have a dynamic role in early detection of many behavioral patterns of users. The objective of this systematic literature review emphasizes in the role of AI wearable devices in early symptom detection of burnout in student population. (2) Methods: A systematic literature review was designed based on PRISMA guidelines. The general extracted aspect was to exploit all the current related research evidence about the effectiveness of wearable devices in student population. (3) Results: The reviewed studies document the importance of physiological monitoring, AI-driven predictive models, with the collaboration of self-reported scales in assessing mental well-being. It is reported that stress is the most frequently studied burnout-related symptom. Meanwhile, heart rate (HR) and heart rate variability (HRV) being the most commonly used biomarkers that can be monitored and evaluated in early burnout detection. (4) Conclusions: Despite the promising potential of these technologies, several challenges and limitations must be addressed to enhance their effectiveness and reliability.