Search Results (100)

Background/Objectives: It is essential to explore and disseminate positive deviance strategies that promote resilience, mindfulness, and well-being beyond stress and burnout reduction strategies for residents and physicians who experience high levels of occupational stress. This scoping review maps studies that investigate positive deviance strategies to enhance the well-being of residents and physicians. Methods: A scoping review was conducted by PRISMA guidelines to identify English-language studies on strategies for physician well-being. PubMed, MEDLINE, Embase, and ERIC were searched using terms related to well-being, coping, and medical education. Results: Among the 38 studies included, 17 (44.7%) targeted physicians in graduate medical education (GME), while 19 (50%) focused on continuing medical education (CME). Positive deviance strategies were identified in 26 studies and were most frequently implemented at the microsystem level, such as small group interventions (e.g., coaching, mentoring, and workshops). These strategies addressed individual and organisational factors that contribute to physician well-being and were associated with improvements in life satisfaction, resilience, professional identity, and psychological safety. The review found that positive deviance strategies were often proactive, values-driven, and disseminated organically over time, emphasising the importance of longitudinal engagement and sustained institutional support. Conclusions: This scoping review highlights the growing use of positive deviance strategies, especially at the microsystem level, to promote physician well-being. These approaches emphasise sustainable, values-driven practices and may offer effective, context-sensitive solutions within healthcare systems. Full article

The present study focuses on investigating the contribution of puppetry as a pedagogical and psychosocial tool in special education, addressing the literature gap in the systematic documentation of the experiences of special education teachers, concerning its use in daily teaching practice. The main objective is to capture the way in which puppetry enhances the learning, social and therapeutic support of students with complex educational and psychosocial needs. The study employs a qualitative phenomenological approach, conducting semi-structured interviews with eleven special education teachers who integrate puppetry into their teaching. Qualitative data were analyzed using thematic analysis. The findings highlight that puppetry significantly enhances cognitive function, concentration, memory and language development, while promoting the active participation, cooperation, social inclusion and self-expression of students. In addition, the use of the puppet acts as a means of psycho-emotional empowerment, supporting positive behavior and helping students cope with stress and behavioral difficulties. Participants identified peer support, material adequacy and training as key factors for effective implementation, while conversely, a lack of resources and time is cited as a key obstacle. The integration of puppetry in everyday school life seems to ameliorate a more personalized, supportive and experiential learning environment, responding to the diverse and complex profiles of students attending special schools. Continuous training for teachers, along with strengthening the collaboration between the arts and special education, is essential for the effective use of puppetry in the classroom. Full article

Effective language processing relies on pattern detection. Spanish monolinguals predict verb tense through stress–suffix associations: a stressed first syllable signals present tense, while an unstressed first syllable signals past tense. Low-proficiency second language (L2) Spanish learners struggle to detect these associations, and we investigated whether they benefit from game-based training. We examined the effects of four variables on their ability to detect stress–suffix associations: three linguistic variables—verbs’ lexical stress (oxytones/paroxytones), first-syllable structure (consonant–vowel, CV/consonant–vowel–consonant, CVC), and phonotactic probability—and one learner variable—working memory (WM) span. Beginner English learners of Spanish played a digital game focused on stress–suffix associations for 10 days and completed a Spanish proficiency test (Lextale-Esp), a Spanish background and use questionnaire, and a Corsi WM task. The results revealed moderate gains in the acquisition of stress–suffix associations. Accuracy gains were observed for CV verbs and oxytones, and overall reaction times (RTs) decreased with gameplay. Higher-WM learners were more accurate and slower than lower-WM learners in all verb-type conditions. Our findings suggest that prosody influences word activation and that digital gaming can help learners attend to L2 inflectional morphology. Full article

Deaf and hard-of-hearing (DHH) children are often reported to show deficits on working memory (WM) tasks. These deficits are often characterized as contributing to their struggles to acquire spoken language. Here we report a longitudinal study of a large (N = 103) sample of DHH children who acquired American Sign Language (ASL) as their first language. Using an n-back working memory task, we show significant growth in WM performance across the 7–13-year-old age range. Furthermore, we show that children with early access to ASL from their DHH parents demonstrate faster WM growth and that this group difference is mediated by ASL receptive skills. The data suggest the important role of early access to perceivable natural language in promoting typical WM growth during the middle school years. We conclude that the acquisition of a natural visual–gestural language is sufficient to support the development of WM in DHH children. Further research is required to determine how the timing and quality of ASL exposure may play a role, or whether the effects are driven by acquisition-related corollaries, such as parent–child interactions and maternal stress. Full article

Background/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through unstructured narratives in clinical notes, online forums, and telehealth platforms. This study proposes a machine learning pipeline (sleepCare) that classifies sleep-related narratives into clinically meaningful categories, including stress-related, neurodegenerative, and breathing-related disorders. The proposed framework employs natural language processing (NLP) and machine learning techniques to support remote applications and real-time patient monitoring, offering a scalable solution for the early identification of sleep disturbances. Methods: The sleepCare consists of a three-tiered classification pipeline to analyze narrative sleep reports. First, a baseline model used a Multinomial Naïve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. Finally, a transformer-based model (BERT) was fine-tuned to extract contextual embeddings, using the [CLS] token as input for SVM classification. Each model was evaluated using stratified train-test splits and 10-fold cross-validation. Hyperparameter tuning via GridSearchCV optimized performance. The dataset contained 475 labeled sleep narratives, classified into five etiological categories relevant for clinical interpretation. Results: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of 81% on the test set. Class-wise F1-scores ranged from 0.72 to 0.91, with the highest performance observed in classifying normal or improved sleep (F1 = 0.91). The macro average F1-score was 0.78, indicating balanced performance across all categories. GridSearchCV identified the optimal SVM parameters (C = 4, kernel = ‘rbf’, gamma = 0.01, degree = 2, class_weight = ‘balanced’). The confusion matrix revealed robust classification with limited misclassifications, particularly between overlapping symptom categories such as stress-related and neurodegenerative sleep disturbances. Conclusions: Unlike generic large language model applications, our approach emphasizes the personalized identification of sleep symptomatology through targeted classification of the narrative input. By integrating structured learning with contextual embeddings, the framework offers a clinically meaningful, scalable solution for early detection and differentiation of sleep disorders in diverse, real-world, and remote settings. Full article

The aim of this study was to test the Implicit Prosody Hypothesis using a reading corpus, i.e., a text without experimental manipulation labelled with eye-tracking parameters. For this purpose, a bilingual Croatian–English reading corpus was analysed. In prosodic terms, Croatian and English are at the opposite ends of the spectrum: English is considered a time-framed language, while Croatian is a syllable-framed language. This difference served as a kind of experimental control in this study on natural reading. The results show that readers’ eyes lingered more on stressed syllables than on the arrangement of stressed and unstressed syllables for both languages. This is especially pronounced for English, a language with greater differences in the duration of stressed and unstressed syllables. This study provides indirect evidence in favour of the Implicit Prosody Hypothesis, i.e., the idea that readers are guided by their inner voice with its suprasegmental features when reading silently. The differences between the languages can be traced back to the typological differences in stress in English and Croatian. Full article

One in four students in the United States is part of an immigrant family. The purpose of this study is to enhance our understanding of the barriers that immigrant students experience in US public schools by critically analyzing how newspapers portray barriers to success, as the goals and processes used in media differ from those of peer-reviewed research. The authors used a document analysis, a qualitative research methodology, and reviewed 67 newspaper articles on immigrant children struggling in US schools. The results show that immigrant students struggle with language barriers, discrimination, mental health, financial stress associated with higher education in the US, lack of preparedness and resources to provide education, lack of familiarity with policy, lack of cultural knowledge about the US, lack of parent involvement, and work and familial obligations. Results also indicate that newspapers published more articles about immigrant struggles during certain time periods, such as Spring 2015 through Winter 2016 and again Summer 2020 through Spring 2021. The paper provides implications for (1) research, suggesting a need for more qualitative primary data collection, (2) practice, including enhanced training, improved mental health referrals and collaborations, and (3) policy, which could include welcoming policies at the school level and advocacy efforts for immigrant student rights under the incoming presidential administration. Full article

Understanding the coupled evolution mechanisms of stress, fracture, and seepage fields in overburden strata is critical for preventing water inrush disasters during fully mechanized mining in deep coal seams, particularly under complex hydrogeological conditions. To address this challenge, this study integrates laboratory experiments with FLAC3D numerical simulations to systematically investigate the multi-field coupling behavior in the Luotuoshan coal mine. Three types of coal rock samples—raw coal/rock (bending subsidence zone), fractured coal/rock (fracture zone), and broken rock (caved zone)—were subjected to triaxial permeability tests under varying stress conditions. The experimental results quantitatively revealed distinct permeability evolution patterns: the fractured samples exhibited a 23–48 × higher initial permeability (28.03 mD for coal, 13.54 mD for rock) than the intact samples (0.50 mD for coal, 0.21 mD for rock), while the broken rock showed exponential permeability decay (120.32 mD to 23.72 mD) under compaction. A dynamic permeability updating algorithm was developed using FISH scripting language, embedding stress-dependent permeability models (R² > 0.99) into FLAC3D to enable real-time coupling of stress–fracture–seepage fields during face advancement simulations. The key findings demonstrate four distinct evolutionary stages of pore water pressure: (1) static equilibrium (0–100 m advance), (2) fracture expansion (120–200 m, 484% permeability surge), (3) seepage channel formation (200–300 m, 81.67 mD peak permeability), and (4) high-risk water inrush (300–400 m, 23.72 mD stabilized permeability). The simulated fracture zone height reached 55 m, directly connecting with the overlying sandstone aquifer (9 m thick, 1 MPa pressure), validating field-observed water inrush thresholds. This methodology provides a quantitative framework for predicting water-conducting fracture zone development and optimizing real-time water hazard prevention strategies in similar deep mining conditions. Full article

Anxiety, depression, and stress are prevalent psychosocial manifestations in Long COVID, and understanding their global impact can guide safe, effective, and evidence-based interventions. This study reviewed the literature to analyze the prevalence indicators and predictors of anxiety, depression, or stress experienced by adults and older adults with Long COVID. This systematic prevalence review was conducted using the databases MEDLINE via PubMed^®, CINAHL-EBSCO, Web of Science, Scopus, EMBASE, LILACS, and BDENF. Observational studies that assessed anxiety, depression, or perceived stress in adults and older adults with Long COVID were included, with no restrictions on time or language. Two reviewers independently conducted the selection process. Full texts were analyzed for their eligibility potential. Methodological quality was assessed using the JBI Critical Appraisal Checklist for Studies. Ten observational studies with moderate methodological quality were included. Anxiety and depression were the most prevalent psychosocial symptoms in Long COVID, reported in mild, moderate, and severe cases of COVID-19 infection. Prevalence rates reached up to 47.8% for anxiety, 37.3% for depression, and 23% for stress. The combined analysis revealed a pooled prevalence of 15.3% (95% CI: 10.8% to 20.2%). Being female, having pre-existing mental disorders or associated clinical comorbidities, experiencing severe infection in the acute phase, and receiving intensive care were predictors of greater mental burden. The experience of anxiety, depression, and stress in prolonged COVID-19 was reported in countries with different income levels and was disproportionately experienced, especially by women and individuals with associated clinical conditions or psychopathological comorbidities. Full article

The COVID-19 pandemic may have disproportionately affected forcibly displaced people due to parallel uncertainties such as visa insecurity and family separation. This study explicitly examined whether different sources of uncertainty contributed in specific ways to increased psychological symptoms for refugees during the pandemic. A large cohort of 733 refugees and asylum seekers settled in Australia completed a mental health survey in June 2020 (T1) and 12 months later in June 2021 (T2). Using cross-lagged panel modelling, we tested changes in post-traumatic stress (PTS), depression and anxiety symptoms, visa status, family separation and COVID-19 uncertainty stress, and the contribution of intolerance of uncertainty (trait prospective and inhibitory), controlling for age, sex, trauma exposure, language, and time in Australia. Visa status and family separation stress at T1 predicted increased depression (bidirectional pathways) and PTS symptoms at T2 (unidirectional pathways), respectively. Visa status uncertainty at T1 was also associated with increases in COVID-19 and family separation stress at T2. Intolerance of uncertainty showed limited associations with symptoms and stressors. Findings demonstrate that different forms of refugee uncertainty had specific impacts on psychopathology during the first year of the COVID-19 pandemic. Refugees facing diverse kinds of stress may benefit from individual, community, and policy level support targeted to their specific circumstances and mental health needs during future crises. Full article

As awareness of mental health issues grows, there is an increasing demand for innovative tools that provide personalized emotional support. By introducing vFerryman, an AI-driven companion system was designed to enhance emotional well-being in this study. The system integrates advanced natural language processing and machine learning technologies into the CrewAI framework. Multiple AI agents were used to deliver personalized, real-time emotional responses. By utilizing large language model operations (LLMOps), vFerryman optimizes the performance of large language models to dynamically adapt to users’ emotional feedback. A key feature of the system is its calming aquarium module, which offers a soothing visual environment to alleviate stress and anxiety. Additionally, vFerryman includes a social interaction platform that fosters emotional connections and shared experiences among users. The effectiveness of vFerryman in improving emotional well-being and facilitating social interaction was evaluated while identifying areas for further technical enhancement and practical applications in emotional support systems. Full article

The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85–90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76, p < 0.01), extended work hours (>10 h/day, +40% risk), and inadequate training (β = 0.68, p < 0.05). Adaptive workload scheduling and fatigue monitoring reduced burnout prevalence by 28%. Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. The results demonstrate AI’s effectiveness in workplace safety, predicting, classifying, and mitigating risks. Reinforcement learning-based adaptive monitoring optimizes workforce well-being. Expanding predictive-driven occupational health frameworks to broader industries could enhance safety protocols, ensuring proactive risk mitigation. Future applications include integrating biometric wearables and real-time physiological monitoring to improve predictive accuracy and strengthen occupational resilience. Full article

With the rapid development of generative artificial intelligence (AI) technologies, large language models have been developed and used in education. In this study, we employ the Google Gemini AI tool (version 1.0) to annotate teachers’ programming of teaching materials. When students learned these annotated teaching materials, the ThinkGear ASIC module (TGAM) and galvanic skin response (GSR) sensors were deployed to measure student mindfulness meditation, relaxation levels, and learning stress. We constructed a backpropagation neural network (BPNN) model with three hidden layers to predict student concentration and relaxation levels using GSR data and the time that students spent answering questions. In the developed system, we deployed a Node-Red dashboard to monitor all sensing data and predict results for mindfulness meditation and relaxation levels. The results were stored in an SQLite database. The BPNN model effectively predicted students’ mindfulness meditation and relaxation levels. For multiple-choice questions about teaching materials, the mean absolute error (MAE) of the BPNN model was 14.29 for mindfulness meditation and 10.54 for relaxation. Full article

Background/Objectives: Swedish schools are mandated to counteract gender norms that restrict students’ life opportunities. School personnel also bear the responsibility of fostering students’ democratic responsibilities and healthy behaviors, which is crucial not only for their mental wellbeing but also for their academic performance, as stressed by the European Commission. Aim: The purpose of the present study is to explore adolescents’ performativity of gender when discussing social barriers to mental and emotional wellbeing. Methods: Fifty adolescents were interviewed in small gender-divided groups, and the transcribed text was analyzed using thematic analysis. Theoretically, interactionist perspective and gender analytic discourses are applied. Results: Emotional barriers to mental wellbeing were identified based on too cogent gender norms. Boys describe challenging each other and the environment by using a social facade that includes “stoneface” and harsh language, seldom showing sadness, even among close friends. The girls’ facade includes maintaining a “happy face” and trying to be attractive. Both genders underline the need for belonging, and most of them fear social exclusion from peers. According to the interviewees, it is socially acceptable for girls to display most feelings, even mental difficulties such as anxiety or phobia, but among boys, gender norms still hinder them from showing emotional vulnerabilities such as sadness and risking exclusion. Conclusions: Young people’s emotional wellbeing needs to be further developed and included in the curriculum. It is time for adults to focus on boys’ sadness and depressive emotions, as well as girls’ aggressiveness and frankness rather than their appearance, to push the river of equality forward. Full article

This systematic review aims to investigate the ways in which assistive and developing technologies can help children and adolescents with autism spectrum disorder (ASD) experience less stress and neurophysiological distress. According to recent CDC data, the prevalence of ASD in the United States has climbed to 1 in 36 children. The symptoms of ASD can manifest in a wide range of ways, and the illness itself exhibits significant variations. Furthermore, it has been closely linked to experiencing stress and worry in one’s life, which many people refer to as sensory processing disorder (SPD). SPD is a disorder that describes how people behave when they are exposed to environmental stimuli that they may not normally process by feeling more intense than what is causing them to worry and distress. One of the most significant limiting factors that can prevent someone from engaging in what they need to do in their everyday lives is stress. Individuals with ASD deal with stress on a regular basis, which has a big impact on how they function. In order to address a significant research vacuum concerning the use of assistive and emerging technologies to reduce stress in individuals with ASD, this systematic review aims to investigate performance, measuring techniques, and interventions by gathering data from the past 10 years. In order to determine the research hypothesis, particular research questions, and the inclusion and exclusion criteria for the studies, the research process entails gathering studies through systematic review analysis in accordance with the PRISMA principles. Experimental and observational studies on the use of assistive and emerging technologies for stress and anxiety management in children and adolescents with ASD that were published only in English met the inclusion criteria. Research not directly related to stress and anxiety outcomes, articles published in languages other than English, and research conducted outside of the designated time frame were also excluded. The study’s findings demonstrated that the technologies under examination had beneficial impacts on reducing stress; nonetheless, notable limitations were found that could compromise the replication and generalizability of legitimate and dependable applications in their utilization. Full article