Search Results (46)

Deep learning (DL) approaches to natural language processing (NLP) offer powerful tools for creating digital twins (DTs) of patients in psychiatry and neurological rehabilitation by processing unstructured textual data such as clinical notes, therapy transcripts, and patient-reported outcomes. Techniques such as transformer models (e.g., BERT, GPT) enable the analysis of nuanced language patterns to assess mental health, cognitive impairment, and emotional states. These models can capture subtle linguistic features that correlate with symptoms of degenerative disorders (e.g., aMCI) and mental disorders such as depression or anxiety, providing valuable insights for personalized treatment. In neurological rehabilitation, NLP models help track progress by analyzing a patient’s language during therapy, such as recovery from aphasia or cognitive decline caused by neurological deficits. DL methods integrate multimodal data by combining NLP with speech, gesture, and sensor data to create holistic DTs that simulate patient behavior and health trajectories. Recurrent neural networks (RNNs) and attention mechanisms are commonly used to analyze time-series conversational data, enabling long-term tracking of a patient’s mental health. These approaches support predictive analytics and early diagnosis by predicting potential relapses or adverse events by identifying patterns in patient communication over time. However, it is important to note that ethical considerations such as ensuring data privacy, avoiding bias, and ensuring explainability are crucial when implementing NLP models in clinical settings to ensure patient trust and safety. NLP-based DTs can facilitate collaborative care by summarizing patient insights and providing actionable recommendations to medical staff in real time. By leveraging DL, these DTs offer scalable, data-driven solutions to promote personalized care and improve outcomes in psychiatry and neurological rehabilitation. Full article

Sound event detection tasks are rapidly advancing in the field of pattern recognition, and deep learning methods are particularly well suited for such tasks. One of the important directions in this field is to detect the sounds of emotional events around residential buildings in smart cities and quickly assess the situation for security purposes. This research presents a comprehensive study of an ensemble convolutional recurrent neural network (CRNN) model designed for sound event detection (SED) in residential and public safety contexts. The work focuses on extracting meaningful features from audio signals using image-based representation, such as Discrete Cosine Transform (DCT) spectrograms, Cocheagrams, and Mel spectrograms, to enhance robustness against noise and improve feature extraction. In collaboration with police officers, a two-hour dataset consisting of 112 clips related to four classes of emotional sounds, such as harassment, quarrels, screams, and breaking sounds, was prepared. In addition to the crowdsourced dataset, publicly available datasets were used to broaden the study’s applicability. Our dataset contains 5055 audio files of different lengths totaling 14.14 h and strongly labeled data. The dataset consists of 13 separate sound categories. The proposed CRNN model integrates spatial and temporal feature extraction by processing these spectrograms through convolution and bi-directional gated recurrent unit (GRU) layers. An ensemble approach combines predictions from three models, achieving F1 scores of 71.5% for segment-based metrics and 46% for event-based metrics. The results demonstrate the model’s effectiveness in detecting sound events under noisy conditions, even with a small, unbalanced dataset. This research highlights the potential of the model for real-time audio surveillance systems using mini-computers, offering cost-effective and accurate solutions for maintaining public order. Full article

This qualitative research examines how sustainable educational transformation emerges through teachers’ participation in enduring professional learning communities, with a specific focus on inclusive education practices. By investigating the intersection of educational sustainability and professional development, this study analyzed ten teacher communities comprising 156 educators who met regularly throughout the school year. The research revealed how sustainable professional networks create lasting institutional change through four critical stages of teacher transformation: initial resistance to inclusion, recognition of pedagogical misconceptions, development of non-judgmental professional discourse, and establishment of sustainable, inclusive practices. The communities functioned as a sustainable infrastructure that fostered both the environmental and social dimensions of educational development, enabling teachers to build lasting professional confidence and emotional resilience through integrated theoretical learning and practical application. This sustainable approach to professional development helped educators overcome initial resistance to individualized solutions and empowered them to adopt more responsive, inclusive classroom strategies. By creating supportive knowledge-sharing networks that extend beyond initial implementation, these professional communities demonstrated their capacity to generate lasting educational change. The findings illuminate how systematic, collaborative professional development can create sustainable educational frameworks that enhance both teacher growth and inclusive pedagogical approaches, contributing to the broader goals of sustainable educational development. This research offers insights into building sustainable professional learning environments that support long-term institutional change while advancing inclusive education practices. Full article

Multimodal emotion recognition involves leveraging complementary relationships across modalities to enhance the assessment of human emotions. Networks that integrate diverse information sources outperform single-modal approaches while offering greater robustness against noisy or missing data. Current emotion recognition approaches often rely on cross-modal attention mechanisms, particularly audio and visual modalities; however, these methods do not assume the complementary nature of the data. Despite making this assumption, it is not uncommon to see non-complementary relationships arise in real-world data, reducing the effectiveness of feature integration that assumes consistent complementarity. While audio–visual co-learning provides a broader understanding of contextual information for practical implementation, discrepancies between audio and visual data, such as semantic inconsistencies, pose challenges and lay the groundwork for inaccurate predictions. In this way, they have limitations in modeling the intramodal and cross-modal interactions. In order to address these problems, we propose a multimodal learning framework for emotion recognition, called the Hybrid Multi-ATtention Network (HMATN). Specifically, we introduce a collaborative cross-attentional paradigm for audio–visual amalgamation, intending to effectively capture salient features over modalities while preserving both intermodal and intramodal relationships. The model calculates cross-attention weights by analyzing the relationship between combined feature illustrations and distinct modes. Meanwhile, the network employs the Hybrid Attention of Single and Parallel Cross-Modal (HASPCM) mechanism, comprising a single-modal attention component and a parallel cross-modal attention component, to harness complementary multimodal data and hidden features to improve representation. Additionally, these modules exploit complementary and concealed multimodal information to enhance the richness of feature representation. Finally, the efficiency of the proposed method is demonstrated through experiments on complex videos from the AffWild2 and AFEW-VA datasets. The findings of these tests show that the developed attentional audio–visual fusion model offers a cost-efficient solution that surpasses state-of-the-art techniques, even when the input data are noisy or missing modalities. Full article

This participatory action research study identified concrete responsive teaching practices that create sustainable, inclusive environments for vulnerable youth, focusing on two primary research questions: (1) How do teacher–pupil interactions shape learning experiences, and (2) What specific responsive practices create sustainable, inclusive environments? Thematic analysis of data from eight teacher candidates, eight cooperating teachers, and seven pupils revealed a clear typology of sustainable, responsive practices organized into three core categories. The first category, the socioemotional response category, encompasses crucial practices including boundary-balanced relationships (creating safe spaces with clear expectations), ability-focused trust (communicating persistent belief in pupils’ capabilities), and non-abandonment approaches (illustrating unwavering commitment to each pupil regardless of challenges). The second category, the pedagogical response category, captured two essential practices: individualized learning adaptations (tailoring instruction to specific pupil needs) and strategic instructional pausing (recognizing when emotional needs must precede academic content). The third category, the systemic response category, highlighted the importance of collaborative professional networks, where mutual support among educators creates resilient teaching environments that enhance pupil outcomes. These findings underscore how responsive teacher–pupil interactions create inclusive environments that meet pupils’ academic, social, and emotional needs while fostering sustainable educational practices. This research contributes to sustainable education by (1) operationalizing responsiveness into actionable components for practice, (2) illustrating how sustainable, responsive teaching develops through structured collaborative inquiry, and (3) providing a framework showing how responsive relationships specifically support vulnerable youth in inclusive settings. The emerging themes illuminate how empathetic, responsive relationships build sustainable, inclusive learning environments empowering all participants for long-term development. Full article

Teachers’ ability to manage stress and daily challenges is crucial to their professional development and well-being. By examining how Chilean elementary school teachers perceive the growth in their resilient behavior, this study seeks to determine the predominant factors that foster resilience in these individuals. Sixty-three public school teachers participated in semi-structured interviews as part of a qualitative analysis employing deductive and inductive coding. The results show that teacher resilience is built through a complex interaction between personal, professional, and contextual factors. Among the most important factors are self-care, psychosocial support, and an institutional environment that facilitates teachers’ emotional well-being. Relationships with students and their families also support this resilience, creating an atmosphere of trust and collaboration in the classroom. The study highlights that strengthening resilience not only improves the teaching experience but also has a positive impact on students’ well-being and academic performance. Integrating self-care strategies, support networks, and family alliances in teacher training and education policies is essential to developing healthy and sustainable school environments. The study concludes by emphasizing the importance of specific training programs that give teachers effective tools to manage stress and adversity better. Although limited to elementary education in Chile, this study invites future research that expands the analysis to other education levels and cultural settings, offering a more comprehensive view of resilience in education. Full article

This review analyses the evolution of digital creativity from 2000 to 2022, defining it as a dynamic process where digital technologies foster collaboration, inclusion, self-expression, and automation. Based on 29 studies from databases such as Dialnet, UOC, Scopus, the Web of Science, and PsyINFO, and utilising the PRISMA and SPIDER methodologies, the research provides a comprehensive overview of the field. Digital creativity is described as the generation of new and valuable ideas, products, or solutions through digital tools that combine cognitive and socio-emotional skills in collaborative environments. Intuition, understood as the ability to make quick and effective decisions based on pattern recognition and prior experiences, plays a crucial role in the creative process. The study highlights its impact on education, enabling students to explore self-expression and solve problems creatively, merging analogue and digital creativity. In the professional realm, it optimises innovative processes, promoting efficiency and collaboration. The integration of emerging technologies, such as programming and 3D animation, in educational curricula is emphasised to prepare students for future challenges. Additionally, interdisciplinary research is advocated to ensure that digital tools amplify creativity, addressing ethical issues such as intellectual property and the social implications of automated creativity. Finally, the current trends such as game-based learning, innovation driven by social networks, and artificial intelligence are examined, proposing directions for future research. Full article

The implementation of evidence-based practice (EBP) is crucial for improving patient outcomes and healthcare delivery, yet it faces significant challenges in acute care settings due to organisational barriers, resource limitations, and leadership complexities. This study explores how ward managers (WMs) facilitate knowledge utilisation (KU) and promote EBP adoption in these environments. A longitudinal qualitative case study was conducted over eight months in two acute care hospitals in the East Midlands, England. Data were collected through semi-structured interviews with 23 WMs, nonparticipant observations, and document analysis. Thematic analysis was used to identify key findings. Six themes emerged: navigating leadership challenges, overcoming organisational and resource barriers, sustaining EBP through learning networks, integrating technology, tailoring EBP to patient-centred care, and providing emotional support for staff. Hybrid leadership strategies, combining directive and collaborative approaches, were critical in addressing barriers, fostering engagement, and embedding EBP into workflows. Mentorship and resource management also played pivotal roles. The study highlights the need for tailored leadership strategies, innovative resource utilisation, and sustainable learning networks to overcome systemic challenges and promote EBP. These findings provide actionable insights for fostering evidence-informed care environments in resource-constrained acute care settings. Full article

The article examines the socio-educational barriers and facilitators faced by families of children with cochlear implants (CI) in Andalucía (a region in southern Spain). Through a qualitative approach based on surveys and interviews, three main categories of limitations are identified: access to and quality of services, parental stress related to caregiving and communication, and socioeconomic and individual factors. Among the facilitators, financial support, family-professional collaboration, and inclusive practices in educational settings are highlighted. The findings underscore challenges such as the lack of coordination between medical and educational services, the need for teacher training, and the emotional and financial impact on families. Conversely, effective strategies such as family support networks, specialized hospital coordinators, and inclusive educational practices are emphasized. The study concludes that improving access to comprehensive services and fostering collaboration between educational and healthcare agents is essential to promote inclusive education and the well-being of families and children with CI. This work advocates for a critical and participatory perspective in the development of inclusive policies. Full article

This article addresses the inherently relational nature of musical agency, drawing upon interdisciplinary research. It argues that music does not exist in isolation but within social and emotional contexts shaped by a network of relationships among musicians. These interactions create a collaborative space that transcends mere technical execution, fostering a collective experience enriched by shared sensitivity and emotional engagement. Consequently, musical practice, whether compositional or performative, entails a moral responsibility, particularly challenging the perspectives of Levinas; Bauman; and Wilde, who traditionally separated art from ethics. As outlined in Nielsen and Cobussen’s work “music contributes to a better understanding of one’s place within the world, and thus, to an ethical sensibility”, emphasizing how musical practice cultivates ethical awareness through its relational nature. Music is thus conceived as a moral endeavor, where care extends not only to the music itself but also to the individuals involved in its creation and performance. Moreover, this moral responsibility extends to future generations, as musical tradition is conceived as a temporal bridge linking the past, the present, and the future. The tradition is composed of individuals actively engaged in the practices that shape the tradition or traditions. Musicians bear an intergenerational responsibility toward those who will inherit and reinterpret current practices—a responsibility not grounded in direct reciprocity, but in what Randall describes as “conceptual ties”. The continuity of musical tradition and its aesthetic value depend on the care and responsibility each generation shows toward both the participants and the evolving cultural products of this process. Thus, the discussion elucidates the ethical dimensions of musical agency and practice, particularly in Western classical music, and highlights how our obligations to future generations are intrinsic to the practice itself, thereby contributing to broader debates on intergenerational care ethics. Full article

Background: The evolution of women’s football over the past three decades has been remarkable in terms of development, visibility, and acceptance, transforming into a discipline with growing popularity and professionalization. Significant advancements in gender equality and global visibility have occurred, and the combination of emerging talent, increasing commercial interest, and institutional support will continue to drive the growth and consolidation of women’s football worldwide. Methods: The purpose of this study was to present a bibliometric analysis of articles on the evolution of women’s football in terms of scientific production as well as its causes and motivations over the past 30 years (1992–2024). A total of 128 documents indexed in the Web of Science database were reviewed. Outcome measures were analyzed using RStudio version 4.3.1 (Viena, Austria) software and the Bibliometrix data package to evaluate productivity indicators including the number of articles published per year, most productive authors, institutions, countries, and journals as well as identify the most cited articles and common topics. Results: Scientific production on women’s football has shown sustained growth, particularly since 2010. Key research areas have focused on injury prevention, physical performance, psychosocial factors, motivation, and leadership. The United States, the United Kingdom, and Spain have emerged as the most productive countries in this field, with strong international collaboration reflected in co-authorship networks. Conclusions: The study revealed a clear correlation between the evolution of women’s football and the increase in scientific production, providing a strong foundation for future research on emerging topics such as the importance of psychological factors, sport motivation and emotional well-being on performance, gender differences at the physiological and biomechanical levels, or misogyny in social networks, thus promoting comprehensive development in this sport modality. Full article

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia’s structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder’s heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI’s integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry. Full article

This study was conducted to identify the key elements of transitional care for families with pre-term infants in South Korea. We used an integrative review methodology proposed by Whittemore and Knafle. During the problem identification step, the review question was constructed via the population, intervention, outcome, and timeframe (PIOT) format. During the literature search step, integrative reviews of the published literature from nine electronic databases were undertaken and a total of 14 studies were reviewed that met our inclusion criteria. During the data evaluation step, the quality of the literature was assessed using the Mixed Methods Appraisal Tool (MMAT) developed by Hong et al. We identified three domains and 10 key attributes of transitional care for families of pre-term infants. The parenting empowerment domain included growth and development, developmental promotion, nutrition, safe environment, general parenting, and parent–infant interaction. The emotional support domain included counseling, advocacy, and community network. The social support domain included professional collaboration. Efforts should continue to further develop programs and policies to enhance transitional care for families of pre-term infants that reflect South Korean nursing practices. Full article

In the last decade data-based decision making has been promoted to stimulate school improvement and student learning. However, many teachers struggle with one or more elements of data-based decision making, as they are often not data literate. In this exploratory study, professional learning networks are presented as a way to provide access to data literacy that is not available in schools. Through interviews with scientific experts (n = 14), professional learning networks are shown to contribute to data-based decision making in four ways: (1) by regulating motivation and emotions throughout the process, (2) by encouraging cooperation by sharing different perspectives and experiences, (3) increasing collaboration to solve complex educational problems, and (4) encouraging both inward and outward brokering of knowledge. The experts interviewed have varying experiences on whether professional learning networks should have a homogenous and heterogenous composition, the degree of involvement of the school leaders, and which competencies a facilitator needs to facilitate the process of data-based decision making in a professional learning network. Full article

Introduction: Peers in the alcohol and other drug sectors possess lived–living experience (LLE) crucial for shaping community care. However, genuine consumer collaboration is often confounded by stigma. This study examined peers’ perceptions, exploring their experiences regarding workforce dynamics, policy implications, and impacts on health equity. In presenting the research, we sought to synthesise the research methods and illustrate the methodological innovation and knowledge production in substance use research through authentic collaboration. Methods: We purposively sampled peer networks and community organisations, involving peer-researchers in planning, design, and analysis. We conducted semi-structured digital interviews with 18 peers and applied iterative coding to analyse the data. Results: This collaborative process provided nuanced insights into sectoral challenges. Peers expressed emotional strain revisiting personal substance use experiences, blurring personal and professional boundaries. Tokenistic peer involvement critiques underscored the need for genuine leadership and organisational support. Conclusion: We advocate for a shift towards equitable and inclusive policy development through both organisational and systemic restructuring. However, these changes are hamstrung by broader policy frameworks, which require a shift to peer-led principles, ensuring the expertise of peers is genuinely valued. Policymakers should invest in expanding peer frameworks, acknowledging the diversity within communities of people who use drugs to improve health equity and public health outcomes. This innovative approach to substance use research emphasises the transformative impact of integrating LLE into research. Full article