Gülsüm Zekiye Tuncer

Aim: This study aimed to evaluate the effectiveness of a psychiatric nurse–led, AI-supported online group guided self-help programme on eating behaviours, body image, and body mass index (BMI) among overweight and obese women nurses. Method: A randomised controlled experimental design with pre-test, post-test, and three-month follow-up assessments was employed. Forty-four overweight and obese women nurses were randomly assigned to intervention (n = 22) and control (n = 22) groups. The intervention consisted of an 8-week online group guided self-help programme supported by a large language model (LLM), under the leadership of a psychiatric nurse. Participants did not interact directly with the LLM; all AI-generated feedback was clinically reviewed and delivered by the researcher. Results: The intervention group demonstrated significant reductions in emotional eating and external eating, along with significant improvements in body image scores and adaptive coping strategies. A significant decrease in BMI was also observed from baseline to three-month follow-up. Between-group comparisons at post-test and follow-up indicated statistically significant differences favouring the intervention for eating behaviours and selected body image–related outcomes, whereas between-group differences in BMI were not statistically significant. Conclusion: A multi-component online group guided self-help intervention incorporating structured AI-assisted feedback under psychiatric nurse leadership may represent a promising approach to improving eating behaviours, body image, and weight-related outcomes among overweight and obese women nurses.

Hemşirelik Öğrencilerine Farkındalıkla Yeme ve Bilinçli Beslenme eğitiminin Gıda ve Beslenme Okuryazarlığı ve Yeme Farkındalığı Üzerine Etkisi ÖZ Amaç: Araştırmanın amacı Gıda ve beslenme okuryazarlığı sınırlı ve yetersiz düzeyde olan hemşirelik birinci sınıf öğrencilerinin bilinçli beslenme ve farkındalıkla yeme eğitiminin gıda ve beslenme okuryazarlığı ve yeme farkındalığı üzerine etkisinin incelenmesidir. Yöntem: Bu çalışma iki aşamadan oluşmaktadır: Birinci aşama tanımlayıcı kesitsel tasarımda, ikinci aşama ise kontrol grubu olmayan yarı deneysel ön test-son test-izlem modeliyle gerçekleştirilmiştir. Bu çalışmanın verileri Aralık 2023 - Ocak 2024 tarihleri arasında toplanmıştır. Birinci aşamada 172 birinci sınıf hemşirelik öğrencisi yer almıştır. İkinci aşamada ise gıda okuryazarlık düzeyi sınırlı ve yetersiz olan 45 öğrenci yer almıştır. Veriler, Yeme Farkındalığı Ölçeği (YFÖ) ve Gıda ve Beslenme Okuryazarlığı Ölçeği (GBOY) ile toplanmıştır. İkinci aşamada Bilinçli Yeme ve Bilinçli Beslenme Eğitimi uygulanmıştır. Bulgular: Çalışmanın ilk aşamasında hemşirelik öğrencilerinin YFÖ ortalaması 3.18 ± .45 olduğu, GBOY Bilgi alt boyut puan ortalamasının 10.27 ± 2.72 olduğu ve %39.5’inin mükemmel düzeyde olduğu; GBOY Tutum alt boyut puan ortalamasının 47.85 ± 6.89 olduğu ve %41.85’inin sınırlı düzeyde olduğu; GBOY Davranış alt boyut puan ortalamasının 29.44 ± 5.27 olduğu ve %51.7’sinin sınırlı düzeyde olduğu görülmüştür. Çalışmanın ikinci aşamasında MEQ ortalaması eğitim öncesi ön testinin, son test ve birinci ay arasındaki ölçümlerine göre düşük olduğu görülmüştür (λ=11.207, p

Background: Conducting bias assessments in systematic reviews is a time‐consuming process that involves subjective judgments. The use of artificial intelligence (AI) technologies to perform these assessments can potentially save time and enhance consistency. Nevertheless, the efficacy of AI technologies in conducting bias assessments remains inadequately explored. Aim: This study aims to evaluate the efficacy of ChatGPT‐4o in assessing bias using the revised Cochrane RoB2 tool, focusing on randomized controlled trials in nursing. Methods: ChatGPT‐4o was provided with the RoB2 assessment guide in the form of a PDF document and instructed to perform bias assessments for the 80 open‐access RCTs included in the study. The results of the bias assessments conducted by ChatGPT‐4o for each domain were then compared with those of the meta‐analysis authors using Cohen's weighted kappa analysis. Results: Weighted Cohen's kappa values showed better agreement in bias in the measurement of the outcome (D4, 0.22) and bias arising from the randomization process (D1, 0.20), while negative values in bias due to missing outcome data (D3, −0.12) and bias in the selection of the reported result (D5, −0.09) indicated poor agreement. The highest accuracy was observed in D5 (0.81), and the lowest in D1 (0.60). F1 scores were highest in bias due to deviations from intended interventions (D2, 0.74) and lowest in D3 (0.00) and D5 (0.00). Specificity was higher in D5 (0.93) and D3 (0.82), while sensitivity and precision were low in these domains. Conclusions: The agreement between ChatGPT‐4o and the meta‐analysis studies in the same RCT assessments is generally low. This indicates that ChatGPT‐4o requires substantial enhancements before it can be used as a reliable tool for bias risk assessments. Clinical Relevance: The AI–based tools have the potential to expedite bias assessment in systematic reviews. However, this study demonstrates that ChatGPT‐4o, in its current form, lacks sufficient consistency, indicating that such tools should be integrated cautiously and used under continuous human oversight, particularly in evidence‐based evaluations that inform clinical decision‐making.

This study aimed to explore the perspectives and experiences of individuals with severe mental disorders regarding psychotropic medication polypharmacy. This study is designed as a descriptive qualitative research involving a sample of 28 individuals with severe mental disorders receiving follow-up care at an adult mental health outpatient clinic of a university hospital in Türkiye between March and December 2024, all of whom were using multiple medications. The inclusion criteria were being an adult diagnosed with schizophrenia or bipolar disorder for at least 12 months and using at least two psychotropic medications for the past two months. Data were collected through in-depth, face-to-face individual interviews. The study data were categorized into six main themes: (1) Disrupted Daily Life Rhythm, (2) Living with Side Effects, (3) Polypharmacy and Illness Perception, (4) Chemicals and Identity, (5) Sustaining Well-being Through Support, and (6) Struggling with the System: Challenges of Medical Procedures. The findings indicate that individuals with severe mental disorders experience significant disruptions in their daily functioning due to polypharmacy, rely on familial and social support to maintain medication adherence, face challenges in complying with healthcare system procedures, and express concerns regarding the psychological and physical effects of psychotropic medications.

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 ( χ 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 χ 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 (α = 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.

Background: As artificial intelligence (AI) technologies rapidly evolve, psychiatric nurses need to be prepared for using and developing this technology in care delivery. Aim: The purpose of this research is to explore psychiatric nurses' perspectives regarding AI applications in supporting both direct and indirect care processes. Methods: A descriptive qualitative method was used in this study. The study sample consisted of 28 psychiatric nurses who agreed to participate in the research between April and October 2024, who had completed or were currently pursuing their master's or doctoral education. Data were collected through individual face‐to‐face/online meetings (via Google Meets application) with psychiatric nurses. Audio recordings were taken during the interviews. The audio recordings were transcribed verbatim and content analysis was performed. Results: In this study, psychiatric nurses' perspectives regarding AI applications in care support were categorised under five themes: (1) Limitations of artificial intelligence in meeting patient needs, (2) Enhancing psychiatric nursing care support, (3) Future Perspectives on enhancing psychosocial care, (4) The challenging path of integration into education and care, (5) Uncertainty, unknowns, risks and concerns. Conclusion: This study reveals psychiatric nurses' insights regarding the potential of AI, particularly in terms of developing psychosocial care, and their predictions about challenges that may be encountered in this process. Taking a pioneering role in integrating AI into care processes, psychiatric nurses may open the doors to a new era while contributing to the development of the profession.