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    Evaluation of ChatGPT-4, Gemini, Claude, and Copilot in Generating Nursing Diagnoses Based on NANDA-I Taxonomy II: A Comparative Cross-Sectional Study
    (Wiley, 2025) Tuncer, Metin; Yalcinkaya, Turgay
    Aim To evaluate the capability of large language models to generate nursing diagnoses based on NANDA-I Taxonomy II and assess their performance across domains and overall.Background Large language models are emerging tools in nursing, showing potential to aid in diagnosis generation and education. However, their accuracy and applicability in clinical and educational settings remain underexplored.Methods This cross-sectional comparative study used 10 realistic patient scenarios based on NANDA-I Taxonomy II, covering 12 domains. The study aimed to evaluate the capability of four models to generate nursing diagnoses based on patient scenarios. The responses were assessed by five nursing experts for accuracy and alignment with NANDA-I Taxonomy II in a single-blind evaluation process.Results All models demonstrated similar performance across different domains and overall, with Claude attaining the highest overall performance score. Expert evaluations indicated moderate interrater reliability.Discussion Small variations between models and occasional omissions suggest that expert review is still required before clinical use.Conclusions Large language models are not yet sufficiently reliable for independent use in clinical settings and nursing education. Their application as supportive tools necessitates a cautious approach. Moreover, the development of specialized models designed to address the unique demands of the nursing field would be advantageous.Implications for nursing When large language models are used in nursing practice, their limitations should be considered, and the outputs they produce should be verified by nurses.Implications for nursing policy Ensuring the safe integration of artificial intelligence tools into nursing necessitates the establishment of robust regulatory policies to safeguard patient safety, the deployment of effective systems to monitor models' performance, and the development of comprehensive guidelines and training programs.
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    Nursing students' cybersecurity practices and perceptions and cybersecurity crime awareness: A cross-sectional study
    (Churchill Livingstone, 2025) Yalcinkaya, Turgay; Tuncer, Metin; Tuncer, Gulsum Zekiye; Erol, Ahmet; Yucel, Sebnem Cinar
    Background: Cybersecurity has become a critical issue with the increasing use of digital platforms in healthcare. Understanding nursing students' cybersecurity practices, perceptions, and cybercrime awareness is essential for improving healthcare security and developing strategies to mitigate cyber threats. Aim: This study aimed to determine nursing students' cybersecurity practices, perceptions, and cybercrime awareness. Design: A descriptive cross-sectional design was used. Setting: The study was conducted between April and June 2024 at a School of Nursing within a public university in T & uuml;rkiye. Participants: A total of 434 undergraduate nursing students participated in the study. Methods: Data were collected face-to-face using a paper-and-pencil technique. The data collection tools used included the Personal Information Form, Cyber Security Scale (CSS), and Cyber Crime Awareness Scale (CAS). Data analysis utilised descriptive statistical methods, Pearson correlation analysis, independent samples t-test, ANOVA, and linear regression analysis. Results: The study revealed that 92.9 % of the students had not received any prior cybersecurity education. The mean CSS score was 87.50 +/- 11.40, and the mean CAS score was 174.75 +/- 36.75. A moderate positive correlation was found between the CSS and CAS scores (r = 0.576, p < 0.01). A positive relationship was found between computer usage skills and CSS scores (r = 0.190, p < 0.01), while a weak negative correlation was observed between internet usage duration and CSS scores (r = -0.095, p < 0.05). No relationships were identified between the CSS score and age, gender, or cybersecurity education. Linear regression analysis showed that higher computer usage skill levels were significantly associated with increased CSS scores (B = 1.129, p < 0.001). Conclusions: The findings highlight the importance of integrating cybersecurity education into the nursing curriculum. Enhancing cybersecurity awareness and practices may help protect patient data and support safer healthcare by better preparing nursing students for cyber threats.
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    Turkish Version of the Sense of Belonging in Nursing School Scale: Validity and Reliability for Nursing Students
    (Springer Publishing Co, 2025) Tuncer, Metin; Tuncer, Gulsum Zekiye; Yalcinkaya, Turgay; Ince, Merve; Yucel, Sebnem Cinar; Khorshid, Leyla
    Background: While a sense of belonging may be necessary for human motivation, the disruption of this sense of belonging among nurses can have implications for patient care and safety. Aim: This article was aimed at assessing the sense of belonging experienced by nursing students in three different settings (clinic, classroom, and student group) by conducting the validity and reliability of the Sense of Belonging in Nursing School (SBNS) scale, adapting it to the Turkish language and culture, and reporting the results. Methods: The study was conducted in a nursing faculty between March and July 2023. The study sample comprised 193 nursing students. We performed the content validity assessment of the 19-item SBNS scale after conducting a structural validity analysis using exploratory factor analysis and confirmatory factor analysis (CFA). The Cronbach's alpha and item-total score correlations were examined to assess the scale's internal consistency. Results: The Kaiser-Meyer-Olkin measure of sampling adequacy for the SBNS instrument was 0.903, and the result of Bartlett's test of sphericity was significant (chi 2 = 3182.764, p = 0.000), indicating that the scale was suitable for factor analysis. The eigenvalue analysis identified a four-factor structure explaining 76.74% of the total variance. These four factors were subsequently named as follows: (1) clinical compliance, (2) social belonging, (3) school support, and (4) clinical support. The goodness-of-fit values for the model obtained from the CFA were chi 2/SD = 2.15, comparative fit index = 0.947, goodness-of-fit index = 0.852, normed fit index = 0.906, incremental fit index = 0.948, and root mean square error of approximation = 0.077, suggesting that the model fit was acceptable, and the four-factor structure was well distributed. The scale exhibited high internal consistency (alpha = 0.933). Discussion: The SBNS scale is a reliable and valid instrument for measuring the sense of belonging experienced by nursing students in three different environments. Further research is needed to establish its predictive validity. Conclusions and Implications for Nursing: This study was conducted, and there was no scale used in the Turkish literature to measure nursing students' sense of school belonging; thus, it is the first in this sense.