FBU GCRIS Standard Database (Fenerbahçe University)
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New Age Negotiations of Power in Türkiye: A Representation of Diverse Profiles
No full textNew Age spirituality intersects with power relations in complex ways. This exploratory study investigates how practitioners in T & uuml;rkiye navigate these intersections. Employing a Bourdieusian framework and utilizing survey data analyzed through cluster analysis, we ity and their interactions with its media and discourses. This analysis reveals distinct approaches to power relations within both the New Age field and the broader socio- political context. Our findings identify three distinct groups: vigilant adopters, negotiated readers, and devoted practitioners. This paper argues that these diverse engagements demonstrate that New Age spirituality in T & uuml;rkiye functions not as a unified challenge to or acceptance of power relations, but as a complex field needs, and social positioning, often resulting in nuanced or even contradictory stances towards societal structures
Evaluation of the Effect of Simulation-Based Training Provided to Nurses in the Hospital Environment on Child Neglect and Abuse: Quasi-Experimental Research
No full textKökkız, Rukiye/0000-0002-9164-7584; Şanci, Yağmur/0000-0002-7272-1005Background: Child neglect and abuse are major public health concerns, and nurses play a critical role in early recognition. Simulation-based training is a promising method to strengthen knowledge and preparedness. Methods: This quasi-experimental pretest-posttest study was conducted with 20 nurses working in a pediatric emergency unit of a state hospital between March and April 2024. Data were collected using a socio-demographic form and the "Scale for Determining the Knowledge Level of Nurses and Midwives in Diagnosing the Symptoms and Risks of Child Abuse and Neglect." Nurses participated in simulation-based training with a structured scenario, followed by debriefing and posttest. Results: Knowledge scores significantly increased after training, particularly in recognizing physical and behavioral symptoms of abuse. However, improvements were limited in identifying children at higher risk of neglect and abuse. Conclusion: Simulation-based training enhances nurses' knowledge of child abuse recognition and should be integrated into nursing education and in-service programs to improve clinical preparedness. (c) 2026 International Nursing Association for Clinical Simulation and Learning. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies
From Data to Autonomy: Integrating Demographic Factors and AI Models for Expert-Free Exercise Coaching
No full textThis study investigates the performance of three deep learning architectures-LSTM with Attention, GRU with Attention, and Transformer-in the context of real-time, self-guided exercise classification, using coordinate data collected from 103 participants via a dual-camera system. Each model was evaluated over ten randomized runs to ensure robustness and statistical validity. The GRU + Attention and LSTM + Attention models demonstrated consistently high test accuracy (mean approximate to 98.9%), while the Transformer model yielded significantly lower accuracy (mean approximate to 96.6%) with greater variance. Paired t-tests confirmed that the difference between LSTM and GRU models was not statistically significant (p = 0.9249), while both models significantly outperformed the Transformer architecture (p < 0.01). In addition, participant-specific features, such as athletic experience and BMI, were found to affect classification accuracy. These findings support the feasibility of AI-based feedback systems in enhancing unsupervised training, offering a scalable solution to bridge the gap between expert supervision and autonomous physical practice
The Relationship Between Spiritual Well-Being, Resilience, and Adherence Among Patients Receiving Hemodialysis Treatment in Türkiye
No full textThis study examined the relationship between spiritual well-being, resilience, and adherence of hemodialysis patients and the factors affecting them. The data were collected from 182 hemodialysis patients receiving treatment in a dialysis center who met the inclusion criteria by purposive sampling method in Istanbul. The data were collected with the patient description questionnaire, which measures patient sociodemographic characteristics and characteristics related to the medical diagnosis, the Spiritual Well-Being Scale, the Brief Resilience Scale, and the End-Stage Renal Failure-Adherence Questionnaire. Gender, educational status, employment status, and mean age of patients were found to be correlated with psychological resilience. Marital status, employment status, cohabitants, and mean age of patients were found to be correlated with spiritual well-being. Gender, number of weekly dialysis sessions, and dialysis competencies were found to be correlated with hemodialysis patients' adherence to their treatment. Hemodialysis patients' adherence was positively correlated with both the faith subscale of spiritual well-being and psychological resilience. According to regression analysis, gender and resilience explained 12.8% of the total variance of adherence. This study determined that resilience is an essential factor in increasing the adherence of hemodialysis patients
Social Media and Financial Decisions: The Influence of Socio-Demographics and Financial Literacy
No full textThis study investigates the predictors of individuals' reliance on social media for financial decision-making within the context of Türkiye's high-inflation environment and the associated surge in retail investor participation. Data were collected via an online survey utilizing the OECD's financial literacy toolkit. The results indicate that gender, family structure, high-risk asset preferences, and financial literacy predict social media usage for financial information. Specifically, males, individuals who invest in stocks or cryptocurrencies, and those with higher financial literacy demonstrate a greater propensity to access financial information on social media; conversely, households with children exhibit lower reliance on social media information. © 2026 American Association of Family and Consumer Sciences
The Influence of Leisure Screen Time on Sleep Patterns and Feeding Behaviors in Primary School Children
No full textThe increasing use of screen-based devices in children's daily lives has raised concerns about their effects on health behaviors such as sleep and feeding. This study investigated the relationship between leisure screen time and sleep and feeding problems in primary school children. A cross-sectional survey was conducted with 322 children aged 7-10 years and their parents. Parents completed questionnaires measuring children's daily leisure screen time, sleep patterns, and feeding behaviors. Results showed that weekend leisure screen time (M = 149.4 minutes/day) was higher than weekdays. Significant positive correlations were found between leisure screen time and both total feeding problem scores (weekdays: r = 0.22; weekends: r = 0.25, p < .01) and sleep disturbances (weekdays: r = 0.29; weekends: r = 0.32, p < .01). The most affected areas were selective eating and sleep initiation/maintenance. Regression analysis revealed that weekend leisure screen time significantly predicted feeding problems (beta = 0.22, p = .001) and sleep disturbances (beta = 0.27, p < .001), explaining 8% and 12% of the variance, respectively. The findings indicate the importance of managing children's screen time - particularly on weekends - to support healthier sleep and eating patterns and guide family-based interventions
Human-Centered Safety and Ergonomic Design for Women in High-Risk Industrial Occupations: A Systematic Review within Intelligent Systems Context
No full textWomen’s participation in high-risk sectors such as mining, construction, transportation, and healthcare continues to increase, yet industrial safety and ergonomics remain dominated by gender-neutral design assumptions. This systematic review synthesizes evidence on physical, psychosocial, and organizational challenges faced by women in hazardous environments through a human-cantered systems lens. Following PRISMA 2020 guidelines, 24 peer-reviewed studies (2010–2025) from Scopus, Web of Science, and PubMed were analyzed. The literature highlights exposure to musculoskeletal disorders (MSDs), PPE mismatch, postural load, and inequitable access to safety resources. Thematic analysis reveals that ergonomic inequalities intersect with exclusion from safety training and organizational barriers in risk management. Findings underscore the need for interdisciplinary approaches integrating ergonomics, intelligent systems, and gender studies to enable safer and more inclusive workplaces for women in high-risk occupations. © 2026 The Authors
Comparative Evaluation of Vision Transformers and Convolutional Networks for Breast Ultrasound Image Classification
No full textAim: Interobserver variability continues to limit the consistency of breast ultrasound interpretation. This study compares two Vision Transformer (ViT) models and two Convolutional Neural Network (CNN) models for automated three-class breast ultrasound classification, with a specific focus on the tradeoff between predictive performance and computational efficiency. Methods: Swin Transformer Base and DeiT Base were evaluated alongside InceptionV3 and MobileNetV3 Large using the public Breast Ultrasound Images (BUSI) dataset, which contains 780 images labeled as benign, malignant, and normal. A consistent on-the-fly augmentation pipeline was applied during training to promote robustness and reduce sensitivity to incidental image variations. Results: Swin Transformer Base achieved the highest test accuracy (0.9167) and F1 score (0.8981). MobileNetV3 Large reached an accuracy of 0.8583 with substantially lower computational demand. The efficiency contrast was pronounced, with Swin requiring 30.33 GFLOPs versus 0.43 GFLOPs for MobileNetV3 Large. Conclusions: On this benchmark, ViT models can yield higher classification performance, while lightweight CNNs offer a strong efficiency profile that may better match deployment-constrained settings. These results suggest that model selection should be guided by both predictive accuracy and operational feasibility within the target clinical workflow. © The Author(s) 2026
Tracking Step Counts in Adolescents with Juvenile Idiopathic Arthritis and Familial Mediterranean Fever: A 3-Month m-Health Monitoring Study Using Smartwatches
No full textTUBITAK 1001-Scientific and Technological Research Projects Support Program [121E690, TUBITAK 1001-Scientific]Acknowledgements: This study was supported by the TUBITAK 1001-Scientific and Technological Research Projects Support Program with project number 121E690."Science Citation Index Expande
