Search Results (306)

Background: This study examined how patients and caregivers perceive and experience AI-based care technologies through text mining analysis. The goal was to identify major themes, sentiments, and value-oriented interpretations embedded in their narratives and to understand how these perceptions align with key dimensions of patient-centered care. Methods: A corpus of publicly available narratives describing experiences with AI-based care was compiled from online communities. Natural language processing techniques were applied, including descriptive term analysis, topic modeling using Latent Dirichlet Allocation, and sentiment profiling based on a Korean lexicon. Emergent topics and emotional patterns were mapped onto domains of patient-centered care such as information quality, emotional support, autonomy, and continuity. Results: The analysis revealed a three-phase evolution of care values over time. In the early phase of AI-mediated care, patient narratives emphasized disruption of relational care, with negative themes such as reduced human connection, privacy concerns, safety uncertainties, and usability challenges, accompanied by emotions of fear and frustration. During the transitional phase, positive themes including convenience, improved access, and reassurance from diagnostic accuracy emerged alongside persistent emotional ambivalence, reflecting uncertainty regarding responsibility and control. In the final phase, care values were restored and strengthened, with sentiment patterns shifting toward trust and relief as AI functions became supportive of clinical care, while concerns related to depersonalization and surveillance diminished. Conclusions: Patients and caregivers experience AI-based care as both beneficial and unsettling. Perceptions improve when AI enhances efficiency and information flow without compromising relational aspects of care. Ensuring transparency, explainability, opportunities for human contact, and strong data protections is essential for aligning AI with principles of patient-centered care. Based on a small-scale qualitative dataset of patient narratives, this study offers an exploratory, value-oriented interpretation of how relational care evolves in AI-mediated healthcare contexts. In this study, care-ethics values are used as an analytical lens to operationalize key principles of patient-centered care within AI-mediated healthcare contexts. Full article

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article

Background: The acceptance of new vaccines under uncertainty—such as during the COVID-19 pandemic—poses a major public health challenge because efficacy and safety information is still evolving. Methods: We propose an integrative analytical framework that combines a theory-based model of vaccine acceptance—the cognitive–affective–normative (CAN) model—with machine-learning techniques (decision tree regression, random forest, and Extreme Gradient Boosting) and SHapley Additive exPlanations (SHAP) integrated into an importance–performance map (IPM) to prioritize determinants of vaccination intention. Using survey data collected in Spain in September 2020 (N = 600), when the AstraZeneca vaccine had not yet been approved, we examine the roles of perceived efficacy (EF), fear of COVID-19 (FC), fear of the vaccine (FV), and social influence (SI). Results: EF and SI consistently emerged as the most influential determinants across modelling approaches. Ensemble learners (random forest and Extreme Gradient Boosting) achieved stronger out-of-sample predictive performance than the single decision tree, while decision tree regression provided an interpretable, rule-based representation of the main decision pathways. Exploiting the local nature of SHAP values, we also constructed SHAP-based IPMs for the full sample and for the low-acceptance segment, enhancing the policy relevance of the prioritization exercise. Conclusions: By combining theory-driven structural modelling with predictive and explainable machine learning, the proposed framework offers a transparent and replicable tool to support the design of vaccination communication strategies and can be transferred to other settings involving emerging health technologies. Full article

This study examines how Artificial Intelligence (AI) is reshaping the role of university teachers and transforming the foundations of academic work in the digital age. Building on the Dynamic Capabilities Theory (sensing–seizing–transforming), the article proposes a theoretical reframing of university teachers’ perceptions of AI. This approach allows us to bridge micro-level emotions with meso-level HR policies and macro-level sustainability goals (SDGs 4, 8, and 9). The empirical foundation includes a survey of 453 Ukrainian university teachers (2023–2025) and statistics, supplemented by a bibliometric analysis of 26,425 Scopus-indexed documents. The results indicate that teachers do not anticipate a large-scale replacement by AI within the next five years. However, their fear of losing control over AI technologies is stronger than the fear of job displacement. This divergence, interpreted through the lens of dynamic capabilities, reveals weak sensing signals regarding professional replacement but stronger signals requiring managerial seizing and institutional transformation. The bibliometric analysis further demonstrates a theoretical evolution of the university teacher’s role: from a technological adopter (2021–2022) to a mediator of ethics and integrity (2023–2024), and, finally, to a designer and architect of AI-enhanced learning environments (2025). The study contributes to theory by extending the application of Dynamic Capabilities Theory to higher education governance and by demonstrating that teachers’ perceptions of AI serve as indicators of institutional resilience. Based on Dynamic Capabilities Theory, the managerial recommendations are divided into three levels: government, institutional, and scientific-didactic (academic). Full article

Background: Blockchain has been extensively discussed for enhancing transparency, traceability, and trust in general; however, there is fragmented empirical evidence available with respect to this issue within maritime logistics. The objective is to integrate and categorize peer-reviewed publications concerning applications of blockchain in maritime logistics and related supply chain domains. Methods: A systematic literature review with PRISMA 2020 was performed in Scopus database, and after a process of screening and eligibility, a total of 78 journal articles published mainly from September 2024 were incorporated. Descriptive and bibliometric analyses were conducted, and VOS viewer-based bibliographic coupling were employed to visualize thematic structure. Results: The review identifies seven research priorities for blockchain in maritime logistics: Technological Interoperability, Economic and Operational Impact, Cybersecurity and Privacy, Adoption and Scalability, Decision-Making and Trust, Environmental Sustainability, and Standardization and Regulatory Frameworks. Blockchain’s primary advantages are enhanced data integrity and visibility, whereas key challenges include interoperability, legal/regulatory uncertainty (e.g., e-doc recognition), high costs, scalability ceilings, integration with legacy systems, and data governance fears. Conclusions: The application of blockchain in maritime logistics depends on combined technical and institutional enabling conditions; an Integrated Blockchain Adoption Framework (IBAF) is suggested, and providing practical guides based on standardization, legal convergence, and hybrid governance modes. Full article

Digital well-being refers to the subjective balance between the benefits and drawbacks of technological connectivity. Although it is a relatively recent construct, research has shown that it can be measured reliably. The Digital Well-Being Scale, comprising three dimensions—Digital Satisfaction, Digital Wellness, and Safe and Responsible Behavior—has been validated in other countries, but not yet in Argentina. This study aimed to adapt and validate the scale in the Argentine context and to examine its associations with Fear of Missing Out (FoMO) and identity bubbles, two variables previously linked to digital experiences. A total of 895 participants (55.2% women; aged 18–65) completed an online survey including the Digital Well-Being Scale, the FoMO Scale, and the Identity Bubble Reinforcement Scale (IBRS-9). Exploratory and confirmatory factor analyses supported the original three-factor structure, and all dimensions showed an adequate internal consistency. A significant negative correlation was found between FoMO and the Digital Wellness dimension, suggesting that individuals with higher FoMO experience lower emotional balance in their digital lives. In contrast, associations between identity bubble dimensions and digital well-being were modest and selective. Only Digital Satisfaction and Digital Wellness were weakly related to social identification and homophily; no relationship was observed with safe digital behavior. These findings support the adapted scale’s psychometric soundness in the Argentine context and provide initial insights into how FoMO and digital identity processes may influence digital well-being. Further research is needed to explore these relationships in more diverse populations and cultural contexts. Full article

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques. Full article

Major depressive disorder (MDD) represents a prevalent mental health condition characterized by prominent attentional biases, particularly toward negative stimuli. While extensive research has established the significance of negative attentional bias in depression, critical gaps remain in understanding the temporal dynamics and valence-specificity of these biases. This study employed eye-tracking technology to systematically examine the attentional processing of emotional faces (happy, fearful, sad) in MDD patients (n = 61) versus healthy controls (HC, n = 47), assessing both the initial orientation (initial gaze preference) and sustained attention (first dwell time). Key findings revealed the following: (1) while both groups showed an initial vigilance toward threatening faces (fearful/sad), only MDD patients displayed an additional attentional capture by happy faces; (2) a significant emotion main effect (F (2, 216) = 10.19, p < 0.001) indicated a stronger initial orientation to fearful versus happy faces, with Bayesian analyses (BF < 0.3) confirming the absence of group differences; and (3) no group disparities emerged in sustained attentional maintenance (all ps > 0.05). These results challenge conventional negativity-focused models by demonstrating valence-specific early-stage abnormalities in MDD, suggesting that depressive attentional dysfunction may be most pronounced during initial automatic processing rather than later strategic stages. The findings advance the theoretical understanding of attentional bias in depression while highlighting the need for stage-specific intervention approaches. Full article

The rehabilitation of burn patients is essential and is intrinsically linked to conventional rehabilitation; the motivational challenges faced by burn patients in maintaining engagement with these rehabilitation programs are well known. It is understood that the use of other resources, particularly technological ones, associated with conventional rehabilitation could overcome these constraints and thereby optimize the rehabilitation program and health outcomes. The objective of this study is to synthesize the available evidence on the use of exergames in rehabilitation programs for burn patients. This systematic review was developed following the guidelines of the Joanna Briggs Institute (JBI). The search was conducted in the following databases: Medline^®, CINAHL^®, Sports Discus^®, Cochrane^®, and Scopus^® during May 2025. The PRISMA Checklist Model was used to organize the information from the selected studies. Seven RCTs were included, involving a total of 236 participants. Outcomes related to the use of exergames in the rehabilitation of burn patients were identified, including increased range of motion, functionality, strength, speed of movement, improved balance, reduced fear and pain, and satisfaction with the technological resource used. It is believed that the results of this review, which confirmed the advantage of using exergames, such as Nintendo Wii, PlayStation, Xbox Kinect, or Wii Fit, to optimize the functionality of burn patients, can support clinical decision-making and encourage the integration of exergames to improve rehabilitation programs for burn patients. Full article

This research presents a systems-thinking analysis of Green Internet implementation in Zimbabwe, integrating the Socioecological Model and Life Cycle Model to provide a multi-faceted understanding of the challenges involved. This study analytically investigates the multilevel socioecological factors and dynamics of the technology life cycle that influence the adoption of sustainable IT principles among institutional actors. Utilizing a hermeneutic phenomenographic approach and data from 102 in-depth interviews, this study reveals a significant lack of awareness, inconsistent implementation, and systemic constraints. A key analytical finding is the dominance of cost-driven procurement and a widespread “technological fetish”, which, combined with the absence of a national e-waste regulation, constitutes a permissive constraint that enables unsustainable practices in the country. The study identifies the lack of a formal e-waste recycling infrastructure and a “fear of disposal” as critical inhibitors in the end-of-life phase of the technology life cycle. Rather than viewing these issues in isolation, this research uses a systems lens to identify the establishment of a national e-waste law with mandatory Extended Producer Responsibility (EPR) as a crucial leverage point. This intervention is a strategic measure to overcome structural impediments and promote sustainable urban development in policy-fragile, low-resource contexts, providing valuable insights for policymakers and contributing to the broader discourse on sustainable ICT adoption in education. Full article

Industry 4.0 is steadily advancing a reality of deepening integration between humans and technology, a phenomenon aptly described by the metaphor of the “technologically augmented human”. This study identifies the digital and personal factors that predict a preference for the “digital centaur” strategy among adolescents and young adults. This strategy is defined as a model of human–AI collaboration designed to enhance personal capabilities. A sample of 1841 participants aged 14–39 completed measures assessing digital centaur preference and identification, emotional intelligence (EI), mindfulness, digital competence, technology attitudes, and AI usage, as well as AI-induced emotions and fears. The results indicate that 27.3% of respondents currently identify as digital centaurs, with an additional 41.3% aspiring to adopt this identity within the next decade. This aspiration was most prevalent among 18- to 23-year-olds. Hierarchical regression showed that interpersonal and intrapersonal EI and mindfulness are personal predictors of the digital centaur preference, while digital competence, technophilia, technopessimism (inversely), and daily internet use emerged as significant digital predictors. Notably, intrapersonal EI and mindfulness became non-significant when technology attitudes were included. Digital centaurs predominantly used AI functionally and reported positive emotions (curiosity, pleasure, trust, gratitude) but expressed concerns about human misuse of AI. These findings position the digital centaur as an adaptive and preadaptive strategy for the technologically augmented human. This has direct implications for education, highlighting the need to foster balanced human–AI collaboration. Full article

Background/Objectives: Digital health technologies are increasingly integrated into perioperative care to standardize information delivery and improve patient empowerment. However, the overall effectiveness of preoperative education depends not only on digital tools but also on interpersonal factors, such as physician gender and clinical experience, which may shape patients’ perceptions and responses to digitally delivered content. Methods: Patients scheduled for elective surgery were included in the iPREDICT randomized trial prior to their preoperative anesthesia assessment. After preoperative anesthetic assessment, the anesthesiologist documented the communication quality and the risks explained. Patients completed a questionnaire to assess their knowledge of anesthesia-related risks and whether the consultation alleviated their fears. Results: A total of 275 included patients were consulted by 94 anesthesiologists, 65% of whom were female. Risk recall was mainly determined by patient-related factors, with online education significantly improving recall over time (β = 1.24, p = 0.034). Anesthesiologists with 1–4 years of clinical experience explained more risks than those with <1 year of professional experience (β = 2.30, p = 0.024). A reduction in post-consultation anxiety was noted when the anesthetist was female (β = 0.21, p = 0.022). Communication was overall rated as good, with higher ratings when anesthetists had more than 10 years of experience (β = 0.09, p = 0.049). Conclusions: Although we have shown with the iPREDICT study (registered in the German CTS; DRKS00032514; on 21 August 2023) that online education improves patients’ recall of anesthesia-related risks, the current sub-analysis emphasizes that interpersonal interactions remain essential for alleviating fears and improving the quality of communication. Together, these findings underscore the complementary roles of digital education and face-to-face consultations in optimizing preoperative preparation. Full article

Background/Objectives: Medical nutrition therapy (MNT) is a crucial component of type 1 diabetes (T1D) management; however, the effectiveness of specific dietary approaches in adults remains unclear due to variations in study design, terminology, and reported outcomes. This scoping review summarizes evidence published between 2015 and 2025 on dietary interventions in adults with T1D, focusing on metabolic and psychosocial outcomes and adherence to international nutritional guidelines. Methods: We searched PubMed, Web of Science, Scopus, and Google Scholar, following the PRISMA-ScR recommendations, to identify observational studies, randomized clinical trials (RCTs), and guidelines involving adults (≥18 years) with T1D. Extracted data included metabolic outcomes (glycated hemoglobin A1c (HbA1c), glycemic variability (GV), insulin dose (ID), lipids, blood pressure, body weight, and others), as well as psychosocial indicators (i.e., quality of life, diabetes-related stress, and fear of hypoglycemia). Results: In total, 41 studies met the inclusion criteria, comprising 18 observational, 14 randomized, and 9 studies that evaluated psychosocial aspects. A low-carbohydrate diet (LCD) reduced HbA1c by 0.3–0.9% and total ID by approximately 15–20% without increasing the incidence of severe hypoglycemia. A low-fat vegan diet and structured carbohydrate counting (CC) programs also improved glycemic and lipid profiles. The Mediterranean diet (MedDiet) and plant-based diet mainly improved diet quality and well-being. The results showed an association between better metabolic control and lower carbohydrate (CHO) intake, as well as higher intakes of fiber and protein. In contrast, a Western diet and high intake of sweets were linked to poorer outcomes. Conclusions: Combining an LCD with education, CC, and modern diabetes technology provides the most consistent benefits for adults with type 1 diabetes (T1D adults). The MedDiet and plant-based diet support diet quality and psychosocial well-being, although current evidence remains limited, primarily due to small sample sizes and short follow-up periods. Full article

Background: Innovative interventions, such as social media platforms and telemedicine, were implemented during the COVID-19 lockdown period for HIV prevention and treatment services. However, limited studies have reported on the facilitators and barriers of these innovations for HIV pre-exposure prophylaxis (PrEP) service continuity. Therefore, this study aimed to identify the barriers and facilitators of the implemented PrEP innovative interventions during COVID-19 among adolescent girls and young women (AGYW). Methods: A qualitative exploratory design was used to conduct semi-structured interviews with twelve stakeholders in the Dr Kenneth Kaunda District, North West Province of South Africa. Participants included various TB HIV Care programme stakeholders, comprising professional nurses, case managers, peer educators, and counsellors. The Consolidated Framework for Implementation Research (CFIR) 2.0 domains and constructs guided the interview questions and the analysis process. Additionally, all interviews were audio-taped, transcribed verbatim, and analyzed through thematic analysis. The facilitators and barriers of the PrEP innovative interventions were categorized according to the five CFIR domains. Results: The findings showed that despite the COVID-19 disruptions in healthcare services, the implemented innovative PrEP interventions enhanced the HIV prevention services. Facilitators included sufficient mobile data, teamwork, clear communication from managers, resilience, and existing media pages that supported social media-based PrEP service continuity. The implementation barriers included service users’ lack of cell phone devices, incorrect personal information, fear of contracting COVID-19, and limited individual movements. Conclusion: Social media and digital technologies played a crucial role in the continuation of HIV PrEP services among AGYW. These evaluations also illustrated the potential of social media platforms to be leveraged for HIV service delivery during periods of disruption, such as the COVID-19 lockdown period, for HIV service delivery. Furthermore, lessons learned from this study are significant and offer practical considerations for sustaining PrEP during service disruptions. Full article

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments. Full article