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- MakaleAçık Erişim2026Olgu Temelli Yapay Zekâ Eğitim Uygulamasına Yönelik Hemşirelik Son Sınıf Öğrencilerinin Görüşleri: Tanımlayıcı Nitel Çalışma
Tuncer, Metin; Tuncer, Gülsüm Zekiye; Yalcinkaya, Turgay
Amaç: Olgu temelli yapay zekâ uygulamalarının hemşirelik eğitimine etkili ve güvenli bir şekilde entegrasyonu, hemşirelik öğren cilerinin çağdaş eğitim ihtiyaçlarını karşılamak açısından önemli bir gerekliliktir. Bu nedenle araştırmanın amacı, hemşirelik son sınıf öğ rencilerinin yapay zekâ destekli olgu temelli eğitim uygulamasına iliş kin deneyimlerini ve görüşlerini derinlemesine incelemektir. Gereç ve Yöntemler: Tanımlayıcı nitel araştırma deseninde yürütülen bu ça lışma, bir devlet üniversitesinde öğrenim gören 15 gönüllü son sınıf hemşirelik öğrencisiyle gerçekleştirilmiştir. ChatGPT-4o tabanlı olgu temelli bir eğitim uygulaması yapan gönüllü katılımcılar ile yarı yapı landırılmış görüşmeler yoluyla veriler yüz yüze bireysel yöntemle top lanmıştır. Görüşmeler, içerik analizi yöntemiyle değerlendirilmiştir. Bulgular: Analiz sonucunda, (1) Öğrenme sürecine katkısı, (2) Kulla nım kolaylığı ve erişilebilirlik, (3) Geleneksel eğitimle kıyaslama ve (4) Güvenilirlik ve sınırlılıklar olarak 4 ana tema ortaya çıkmıştır. Ka tılımcılar, ChatGPT’nin klinik akıl yürütme becerilerini desteklediğini, bilgiye hızlı erişim sağladığını ve yargılamayan bir öğrenme ortamı sunduğunu ifade etmiştir. Ancak bilgi tutarsızlıkları, kaynak belirtme mesi ve komutlara duyarlılık gibi sınırlılıklar da vurgulanmıştır. Sonuç: Çalışma, ChatGPT destekli olgu temelli eğitimin hemşirelik öğrenci lerinin öğrenme sürecine katkı sunduğunu ve geleneksel eğitimi ta mamlayıcı bir potansiyel taşıdığını göstermektedir. Bu tür uygulamaların hemşirelik eğitimine etkili biçimde entegre edilebilmesi için öğretim elemanı rehberliğinde yapılandırılmış senaryolarla yürü tülmesi, öğrencilerin komut oluşturma ve eleştirel düşünme becerileri nin desteklenmesi önerilmektedir.
- Derleme MakaleYalnız künye2026Immunosuppressant adherence levels in kidney transplant recipients measured by electronic monitoring devices: A systematic review and meta-analysis
Tuncer, Metin; Yalcinkaya, Turgay; Erol, Ahmet; Özalp, Cemal
Background: Kidney transplantation improves survival and quality of life in patients with end-stage renal disease; however, long-term graft success depends on adherence to immunosuppressive therapy. Objective: To quantify adherence levels among kidney transplant recipients using electronic monitoring devices. Methods: PubMed, Web of Science, Scopus, and the Cochrane Library were systematically searched for studies published between 2000 and 2025. A random-effects meta-analysis was conducted including studies that assessed adherence using electronic monitoring devices. Results: Seven studies with a total of 1025 kidney transplant recipients were included. The pooled mean adherence measured using electronic monitoring devices was 94.39%. In studies with monitoring durations of three months or longer, adherence decreased to 90.50%. Substantial heterogeneity was observed across studies. Conclusion: Although electronically measured adherence appears generally high, its decline over longer monitoring periods indicates that adherence is dynamic and may decrease over time. However, these estimates should be interpreted cautiously because electronic monitoring devices cannot confirm actual medication ingestion. Furthermore, intentionally non-adherent patients may be less likely to participate in or remain engaged with electronic monitoring studies and may therefore be underrepresented, potentially leading to an overestimation of true adherence. Even modest reductions in adherence may carry important clinical consequences, underscoring the need for sustained monitoring.
- MakaleAçık Erişim2025Evaluation of ChatGPT-4, Gemini, Claude, and Copilot in Generating Nursing Diagnoses Based on NANDA-I Taxonomy II: A Comparative Cross-Sectional Study
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.
- MakaleAçık Erişim2025Nursing students' cybersecurity practices and perceptions and cybersecurity crime awareness: A cross-sectional study
Yalcinkaya, Turgay; Tuncer, Metin; Tuncer, Gülsüm Zekiye; Erol, Ahmet; Cinar Yucel, Sebnem
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 Türkiye. 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.