Search Results (1,314)

In modern society, adolescents experiencing mental health problems are increasing. This study aims to identify barriers to mental health care at the individual, family, school, and staff levels among at-risk adolescents, employing a mixed-methods approach. Given the ethical and practical constraints of engaging at-risk adolescents directly, the study quantitatively analyzed responses to the AMPQ-III-I survey from 47 runaway adolescents, while conducting interviews with eight school nurses serving as proxy informants. The at-risk adolescents were found to be in a state of mental health crisis characterized by somatization, self-harm, excessive digital media use, and peer imitation. Within the family environment, they experienced communication gaps with adults, concerns about mental health stigma, and the risk of disengagement from home and school. Despite experiencing physical and emotional difficulties that hindered their ability to focus on academic work, schools tended to deprioritize mental health, and these adolescents reported notably low utilization of professional counseling. School nurses, although well-positioned to identify at-risk adolescents, expressed barriers such as excessive workload and a lack of communication among teachers. These findings suggest that, to support the growing and intensifying population of at-risk adolescents, an urgent shift in awareness and the alleviation of barriers within the family–school–staff ecosystem is required. Full article

Most artistic expressions are conveyed through images (e.g., painting) and audio (e.g., music), and deep learning has been successfully applied to neural style transfer within each of these modalities. However, there is still a lack of deep models that explicitly learn to transfer style between images and audio. Motivated by synesthesia, which reflects intrinsic connections between vision and hearing in the human brain, we propose a deep hybrid synesthesia model for audio–image style transfer. Our framework consists of two main components: (1) a component conversion module that learns cross-modal mappings between audio rhythm/spectrum and image color/shape in a continuous valence–arousal (VA) emotion space; and (2) a style conversion module that transfers high-level artistic styles between Eastern (ink-wash, shui-mo) and Western painting and their corresponding musical counterparts. We first learn emotion-aware feature networks that align low-level audio and visual components based on shared affective representations, and then model long-term stylistic structures for cross-modal style transfer. Experiments include “seeing the sound” (audio-to-image generation with controllable components) and full audio–image style transformations. Both objective analyses and subjective evaluations suggest that our model can produce cross-modal artworks whose perceived style and emotional content are consistent with human synesthetic impressions. Full article

Background: Environmental health literacy plays an important role in helping individuals recognize environmental risks and adopt sustainable behaviors. Increasing environmental awareness may also influence emotional responses to environmental problems such as climate change. However, the mechanisms linking environmental health literacy to climate change anxiety remain insufficiently explored. This study aimed to examine the relationship between environmental health literacy and climate change anxiety among teachers and to evaluate the mediating role of ecological footprint awareness in this relationship. Methods: This cross-sectional study was conducted with teachers working in public schools in the provinces of Elazığ and Erzincan, Türkiye. Data were collected using a personal information form, the Environmental Health Literacy Scale, the Ecological Footprint Awareness Scale, and the Climate Change Anxiety Scale. Descriptive statistics, group comparison tests, correlation analyses, and mediation analysis based on structural equation modeling were performed to examine the relationships among the study variables. Results: Participants’ mean scores were 35.98 ± 9.12 for the Climate Change Anxiety Scale, 95.37 ± 18.29 for the Environmental Health Literacy Scale, and 118.08 ± 25.92 for the Ecological Footprint Awareness Scale. Environmental health literacy was positively associated with ecological footprint awareness, and ecological footprint awareness was positively associated with climate change anxiety (p < 0.001). Mediation analysis indicated that ecological footprint awareness significantly mediated the relationship between environmental health literacy and climate change anxiety (β = 0.293, 95% CI: 0.112–0.496, p = 0.002). Conclusions: The findings suggest that ecological awareness can serve as a potential mechanism linking environmental knowledge with emotional responses to climate change. Strengthening ecological footprint awareness through environmental education programs for teachers may contribute to both environmental awareness and constructive engagement with climate-related issues. Full article

This article advances a transdisciplinary framework for planetary health education grounded in multispecies responsibility and Indigenous relational ontologies. Addressing the limitations of anthropocentric environmental paradigms, the paper proposes an expanded Stratified Relational Responsibility Model integrating ethical, ecological, and neurobiological dimensions of human–more-than-human relations. The framework bridges insights from environmental ethics, anthropology, and affective neuroscience to examine how relational awareness, emotional regulation, and embodied cognition shape pro-environmental behavior. Four pedagogical pillars are introduced to support behavioral transformation, emphasizing relational perception, affective attunement, ethical reflexivity, and collective responsibility. The article further discusses implementation challenges within Western educational contexts and highlights the need for culturally responsive adaptation. By situating human agency within multispecies networks, the model contributes to ongoing debates in planetary health and sustainability education, offering a theoretically robust and practically oriented approach to fostering ecological responsibility. Full article

Emotion is increasingly incorporated into AI-supported feedback in education, yet less is known about how learners interpret emotion-related messages once they are presented. This paper reports an exploratory classroom-based study comparing three learner-facing strategies for presenting emotion-aware feedback: inference with explanation, inference without explanation, and deliberate non-inference. Using a Wizard-of-Oz procedure embedded in a web-based classroom activity, 78 undergraduate students completed a conceptual quiz, a brief reflection task, and an applied data-analysis task during a 90-min course session. Following the activity, participants evaluated the system on six 7-point Likert outcomes: Perceived Accuracy, Interpretability, Emotional Comfort, Willingness to Reuse, Perceived Usefulness, and Trust. Significant differences were observed across all six outcomes. Across every dimension, the same ordinal pattern emerged: feedback with explanation received the highest ratings, no inference occupied an intermediate position, and inference without explanation was rated lowest. Notably, deliberate non-inference was evaluated more favorably than unexplained inference across all six outcomes. These findings suggest that the learner-facing value of emotion-aware educational AI depends not only on whether emotion is inferred, but on how such inference is presented and contextualized. The study contributes classroom-based evidence that learner interpretation should be treated as an important criterion in evaluating emotion-aware educational AI and that deliberate non-inference can function as a legitimate response strategy when affective claims cannot be presented in an intelligible and contextually grounded way. Full article

Although mentoring programs have been utilized to increase access to STEM in underrepresented communities, there is limited research on the culturally responsive mentoring practices that are needed in high-quality STEM mentoring programs for youth. Grounded in culturally responsive pedagogy and youth mentoring literature on cultural humility, this study examined the culturally responsive mentoring practices of scientist-mentors who work with racially minoritized adolescent mentees from low-income urban communities. Retrospective self-report surveys were administered to 142 scientist-mentor alumni followed by in-depth qualitative interviews of a subset of 35 scientist-mentor alumni. Data analysis revealed three culturally responsive practices that scientist-mentors utilized in their interactions with youth: (a) social-emotional support, (b) role modeling, and (c) youth-oriented science communication. Serving as mentors to youth in the community and spending time with them shifted the mentors’ mindsets. Specifically, mentors showed an increased (a) awareness of inequities in education and (b) commitment to continue engaging with communities. Implications for future research and mentoring program practice are discussed. Full article

This study examines emotional dependency on generative artificial intelligence among vocational high school (VHS) students. Guided by Taiwan’s 108 Curriculum Guidelines, an interactive “Health and Nursing” course on AI reliance was implemented. The sample included 1000 students from five VHSs in central Taiwan (January–February 2026). Data were collected through questionnaires and classroom feedback to assess AI interaction frequency, emotional projection, and perceived effects on relationships and psychological needs. Research data were analyzed using SPSS 22.0 and SmartPLS 4. Findings show that some students displayed moderate to high emotional attachment to AI, particularly for support and stress relief, with blurred ethical boundaries. After the intervention, students reported greater awareness of risks and increased self-reflection. This study concludes that integrating AI literacy with emotional education into curricula is crucial for responsible technology use and healthy relational development. Overall, emotional reliance on AI among VHS students appears statistically significant but bounded, reflecting a balanced pattern of engagement that supports sustainable psychological well-being. Full article

Blended emotion recognition introduces the challenge of identifying not only which emotions are present in an expressive display but also their relative salience. The proposed methodology builds upon the pre-extracted features provided with the dataset and enhances performance through a combination of temporal modeling and multimodal fusion strategies. Unimodal experiments revealed that visual encoders consistently outperformed audio ones, with the multimodal HiCMAE encoder achieving the strongest single-encoder results with 34% presence accuracy and 18.23% salience accuracy. Multimodal fusion further improved performance, with the best validation results obtained using a combination of simple concatenation and attention-based fusion, reaching 47.86% in presence accuracy and 27.92% in salience accuracy. Overall, the proposed methodology surpasses the chosen baseline introduced in the original paper across a k-fold experiment, confirming the effectiveness of multimodal attention-based fusion for the accurate prediction of both emotion presence and salience in blended affective behaviour. The experimental results further indicate that multimodal expression recognition consistently outperforms unimodal approaches, highlighting the complementary nature of cross-modal information. Full article

The advent of social media has transformed brand communication to put storytelling at the center of building engagement and awareness. But the role of long-term brand value in virality is an essential challenge. This paper conducts a bibliometric and thematic analysis from the fields of marketing, psychology, and media studies published between 2015 and 2025, examining the correlation between narrative design and audience response, separating short-term popularity and long-term consumer appeal. The analysis was based on a structured literature review and qualitative methodological framework, using the literature sourced through Scopus, Web of Science, PsycINFO, and Google Scholar published between 2015 and 2025. Thematic coding searched for emotional tones, devices used in the narration, types of metrics, and contextual factors in inclusion and exclusion criteria. The findings indicate a divide in quantitative measures, such as likes and shares, and qualitative measures, such as sentiment and resonance stories. Story elements such as authenticity, the depth of the characters, and video-based content had a major effect on the two types of engagement. Storytelling effectiveness was also mediated by influencer participation, algorithmic interactions, and audience demographics. The results confirm that meaningful storytelling with hybrid metrics contributes to stronger brand–consumer relationships. Future studies ought to shift to predictive modeling and focus on the ability of AI to dictate personalized brand stories in diverse cultures. Full article

Multimodal emotion recognition is challenging because behavioral signals and physiological responses evolve at different temporal rates. Facial expressions and speech often change rapidly after an emotional event, whereas peripheral biosignals such as electrodermal activity, blood volume pulse, and skin temperature exhibit delayed and smoother dynamics. This temporal inconsistency can degrade fusion performance, particularly in real-world recordings with noisy or missing modalities. To address this issue, this study proposes SGMT, an S-PACE Gated Multimodal Transformer for emotion recognition using speech, facial video, and physiological signals. The proposed SGMT introduces S-PACE, a physiology-guided cross-attention mechanism that aligns fast behavioral cues with slower biosignal representations without assuming a fixed temporal delay. A Quality-Aware Gate further improves robustness by adaptively weighting modalities according to signal reliability. The fused representations are processed using a Temporal Swin Transformer and a Perceiver Fusion module for arousal–valence prediction and emotion quadrant classification. Experiments are conducted on the Korean multimodal emotion datasets KEMDy20 and K-EmoCon under different modality settings. SGMT achieves arousal UARs of 68.4% on KEMDy20 and 62.9% on K-EmoCon, with quadrant accuracies of 44.7% and 62.5%, respectively. Ablation studies demonstrate that the proposed alignment and gating strategies provide more stable multimodal fusion than conventional feature concatenation. The results indicate that SGMT effectively adapts to varying modality availability and improves multimodal emotion recognition in naturalistic environments. Full article

Social robots hold promise for enhancing later-life support, but user needs and preferences for multimodal interaction modalities remain underexplored. This study explores awareness, willingness, perceived barriers, and modality–function associations across multiple interaction modalities among middle-aged and older adults, and proposes a conceptual five-layer architecture for design guidance. A questionnaire survey with 199 Chinese respondents (aged 45–64: 89.4%, 65+: 10.6%) examined perceptions of voice, visual, gestural, affective, sEMG, and brain–computer interface interactions. Voice and visual modalities were the most preferred; gesture and affective interactions were moderately accepted; awareness of sEMG was high but may reflect confusion with other sensor technologies; and BCI awareness and willingness were low. Based on survey findings and the literature, a conceptual five-layer architecture is presented to inform future social-robot design. The sample predominantly comprised middle-aged participants, so findings reflect prospective later-life users rather than the broader older-adult population. This study offers user-centered insights into multimodal social-robot interaction and provides design implications for future development rather than evaluating emotional-health interventions. Full article

Spontaneous pregnancy loss is the most common pregnancy complication; 15–25% of clinically confirmed pregnancies end in miscarriage, and when early, undetected pregnancy losses are included, the miscarriage rate can reach as high as 30–60% of all pregnancies. Women who lose their babies in the early stages of pregnancy often experience this as a bereavement. However, in Lithuania, 95.2% of women who had experienced a miscarriage and participated in the study did not receive any psychological support; only 4% of those surveyed stated that they did not need it. However, both in Lithuania and abroad, there is still a lack of research on women’s emotional state following a spontaneous pregnancy loss and the impact of interventions on it. Research question: What experiences emerge in the thematic analysis of women who have experienced a spontaneous miscarriage? A qualitative study was conducted using the inductive thematic analysis method. The study participants underwent a 10-session group art therapy programme. After each art therapy session, the study participants reflected on their experiences related to their miscarriage by analyzing the drawings they had created. Verbal data from the reflections were recorded, then transcribed and analysed according to identified themes. The research participants were four women who had experienced a spontaneous pregnancy loss. The study analyses the reflections of three women who participated in all sessions. The thematic analysis revealed four themes characterising the women’s core experiences of spontaneous pregnancy loss: defensiveness, the grieving process, a complicated relationship with oneself and others, and awareness and finding meaning. Full article

Digital-avatar systems still provide limited control over emotionally expressive behavior in human–computer interaction, especially in Large Language Model (LLM)-based chatbots and virtual assistants with personalized visual embodiments. To address this problem, we propose Multimodal Avatar Generation (MAVAGEN), a multimodal avatar generation framework for synthesizing upper-body digital avatars with personalized appearance and controllable emotional expression. The user specifies the desired gender and age, as well as provides a short text input from which the target emotional state is inferred. MAVAGEN then retrieves an identity image from the HaGRIDv2-1M corpus and generates an avatar clip with synchronized facial expressions, hand gestures, and expressive speech. The framework uses the following six feature streams: textual features, emotion-distribution features, landmark-based pose features, depth-geometry features, RGB-appearance features, and acoustic features. In a quantitative evaluation against recent human animation methods, MAVAGEN achieves the best overall avatar quality, with FID 48.20, FVD 592.00, SSIM 0.741, Sync-C 7.40, HKC 0.929, HKV 25.30, CSIM 0.563, and EmoAcc 0.88. Ablation results show that emotion and acoustic features contribute most to emotional agreement, while landmark-based pose and depth features improve geometric and motion stability. These results support the practical use of MAVAGEN in personalized LLM-based assistants and other emotion-sensitive interactive systems. Full article

Experiential avoidance, defined as unwillingness to deal with personal experiences such as thoughts, emotions, and memories, is closely related to difficulties in emotion regulation. This is because emotional awareness and acceptance are crucial for regulating distressing feelings. Somatic symptoms, referring to bodily sensations such as headaches, nausea, and fatigue with or without any underlying medical condition, are frequently reported among individuals with avoided or dysregulated emotional burden. This cross-sectional correlational study aimed to examine the mediating role of emotion regulation difficulties in the relationship between experiential avoidance and somatic symptoms; we used a sample size of 397 individuals recruited from a non-clinical population with the convenience sampling technique. The measurement instruments were the Brief Experiential Avoidance Questionnaire (BEAQ), Somatic Symptom Scale (SSS-8), and Difficulties in Emotion Regulation Scale (DERS-16). Statistical analysis was conducted using the IBM SPSS 29 statistical program and the SPSS Process Macro 4.2 extension. The results indicate that difficulties in emotion regulation mediated the relationship between experiential avoidance and somatic symptoms controlling for age, education, gender and perceived income and all variables were significantly correlated with each other, including subscales of difficulties in emotion regulation. Limited access to emotion regulation strategies was subscale with an indirect effect on the association between experiential avoidance and somatic symptoms. These findings are expected to guide mental health professionals in consulting clients with somatic symptoms and emotion regulation difficulties. Full article

The representation of the sexuality of people with disabilities in contemporary media is often characterized by stereotypes, omissions, and heteronormative narratives that deny the complexity and richness of their emotional experiences. This essay explores the potential of photovoice-based methodology as a tool of visual empowerment to foster processes of authentic and self-aware self-representation through the immersive dynamic that this methodology can activate. Through an interdisciplinary theoretical approach that combines special pedagogy with recent research on digital media and immersive technologies, the study seeks to understand whether virtual spaces can be configured as protected environments in which people with disabilities have the opportunity to explore and communicate their sexual identity. Photovoice thus becomes a tool of narrative resistance that overcomes barriers and counters mediatized representations, often conveyed through dynamics of ableist cyberbullying and online discrimination. The contribution highlights how the combination of participatory visual storytelling and immersive environments can generate new forms of inclusive media literacy, promoting a Visual Education that recognizes and values the diversity of human experiences. Particular attention is devoted to the educational potential of these tools in the training of educators and social workers, as well as in raising awareness within the broader community. The paper proposes a theoretical and methodological framework for the implementation of visual self-representation projects capable of transforming social perceptions of disability and promoting a culture of authentic and respectful inclusion. Full article