Search Results (5,097)

Background/Objectives: Communication training in undergraduate nursing skills courses is often overshadowed by procedural skill acquisition, leaving limited opportunities for students to develop patient-centered communication skills and engage in reflective learning. This study evaluated a reflection-integrated CICARE communication program embedded in a Fundamentals of Nursing skills course and explored students’ learning experiences. Methods: A quasi-experimental study with an embedded qualitative component was conducted among second-year undergraduate nursing students in China. The quantitative component used a non-equivalent control group design, while reflective journals from the Intervention Group provided qualitative data on students’ learning experiences. Two intact classes were assigned to the Intervention Group (n = 65), and two intact classes were assigned to the Control Group (n = 66). The Intervention Group received an 18-week CICARE-based communication program integrated into skills training, including scenario-based role-play, emotional cue recognition, demonstration videos, feedback, and reflective journals. The Control Group received conventional skills training. Outcomes included communication competence, empathy, professional identity, clinical judgment, and course performance. Analysis of covariance was used to compare post-intervention outcomes after adjustment for baseline scores. Reflective journals from the Intervention Group were analyzed thematically. Results: After adjustment for baseline scores, the Intervention Group showed significantly higher communication competence than the Control Group (adjusted mean difference = 2.699, 95% CI: 0.108–5.291, p = 0.041). Empathy was also higher in the Intervention Group (adjusted mean difference = 3.796, 95% CI: 0.538–7.053, p = 0.023). The between-group difference in professional identity was not statistically significant (p = 0.073). The Intervention Group achieved higher total clinical judgment scores than the Control Group (31.52 ± 5.50 vs. 27.20 ± 6.45, p < 0.001), with significant differences in noticing, interpreting, and reflecting. Course performance was also higher in the Intervention Group. In their reflective journals, students described greater patient-centered awareness, attention to patients’ emotional needs, professional reflection, and motivation for self-directed learning. Conclusions: Integrating CICARE-based communication training with reflective learning may strengthen communication competence, empathy, clinical judgment, and course performance in undergraduate nursing skills education. Further studies with longer follow-up are needed to determine whether the observed differences persist over time and whether professional identity and action-oriented clinical responses improve with longer educational exposure. Registration: This study was not registered. Full article

This quantitative study explores divergent beliefs about word order in American Sign Language (ASL) within the U.S. Deaf community. Although the academic literature commonly characterizes ASL as following a Subject–Verb–Object (SVO) structure, certain Deaf individuals maintain that ASL predominantly uses a Subject–Object–Verb (SOV) order. This perception reflects an ideological positioning of ASL as fundamentally distinct from English and may align with community norms that have historically emphasized SOV structures. Data were collected through an acceptability judgment task with 53 varied sentence structures presented via video. Among 86 Deaf participants, distinct preferences emerged based on ASL acquisition background. Participants with early ASL exposure from Deaf parents preferred the SOV structure, while those with early ASL exposure from hearing parents preferred the SVO structure. This finding suggests that variability in beliefs about acceptable ASL word order, along with factors such as early language exposure and parental input, influences judgments of acceptability. Full article

Background: Depressive symptoms have become a significant public health issue faced by middle-aged and older adults in China. Although previous studies have primarily focused on demographic and socioeconomic factors, the association between modifiable lifestyle behaviors and depressive symptoms still requires further investigation. Objective: To examine the associations between sleep duration, physical activity, digital participation, social participation, and depressive symptoms as well as life satisfaction among middle-aged and older adults in China. Methods: A cross-sectional analysis was conducted using data from the 2020 China Health and Retirement Longitudinal Survey (CHARLS). Depressive symptoms were defined as CES-D-10 scores ≥10, with 13,576 individuals included in the analysis sample. Adjusted prevalence ratios (aPRs) were estimated using modified Poisson regression, while ordinal logistic regression was employed to analyze life satisfaction. The analysis controlled for covariates such as demographics, socioeconomic status, and health status, and accounted for the complex sampling design. Results: The weighted prevalence of depressive symptoms was 44.5%. Sleep deprivation (<7 h/day) was significantly associated with elevated depressive symptoms (aPR = 1.49, 95% CI: 1.33–1.68); participation in recreational activities was significantly associated with reduced depressive symptoms (aPR = 0.86, 95% CI: 0.77–0.96); adequate physical activity was associated with a higher prevalence of elevated depressive symptoms (aPR = 1.17, 95% CI: 1.04–1.32); watching videos (aPR = 0.87, 95% CI: 0.76–0.99) and playing online games (aPR = 0.65, 95% CI: 0.50–0.83) were significantly associated with reduced depressive symptoms; and overall internet use was correlated with lower life satisfaction (OR = 0.90, 95% CI: 0.81–1.00). Women, rural residents, and low-income groups exhibited higher prevalence of elevated depressive symptoms. Conclusion: Sleep deprivation, as well as different types of social engagement, physical activity, and digital behavior, exhibit distinct associations with depressive symptoms in middle-aged and older adults. Behavioral intervention strategies targeting sleep hygiene, leisure activities, and tailored digital behaviors warrant further attention. Full article

Traditional handicraft knowledge is not only embodied in final products and craft procedures but also embedded in the context formed by production spaces, tools, materials, practitioners, and their interrelationships. However, existing digital preservation practices often focus on individual objects or specific data types, making the spatial and semantic relationships among heterogeneous resources insufficiently represented and limiting public understanding of the broader context of craft practices. To address this issue, this paper proposes a digital resource organization and multi-terminal presentation framework. Using 3D point clouds as a unified spatial reference for the site, the framework links images, videos, interviews, craft records, and object-related materials to spatial locations through structured annotation, and visualizes relationships among practitioners, tools, materials, processes, and spaces through node-link representations. The Web-based viewer and CAVE immersive system access the same content dataset, enabling “input once, reuse across terminals”. User feedback suggests that the framework supports the integrated representation of spatial context, craft resources, and associated information within traditional handicraft sites, with relational visualization contributing to a more holistic understanding of craft practices. The framework provides a reusable workflow for organizing, linking, and presenting heterogeneous heritage resources in traditional handicraft transmission sites, offering digital support for a contextual understanding of craft practices. Full article

User-generated videos (UGVs) and professional-generated videos (PGVs) have become important information sources in movie marketing, yet prior research has largely examined user-generated content and professional-generated content as independent drivers of market performance. This study investigates how UGV influences movie box office revenue through PGV and identifies the boundary conditions of this process. Drawing on signaling theory and the elaboration likelihood model, we propose that UGV volume generates social attention and stimulates PGV production, while PGV serves as a more credible quality signal that affects consumers’ viewing decisions. Using panel data on 226 movies released in China from 2024 to 2025 and 245,890 videos collected from Weibo, we test the proposed framework with fixed-effects models, endogeneity tests, and robustness checks. The results show that UGV volume indirectly increases box office revenue through PGV volume. Moreover, UGV creator reputation strengthens the positive relationship between UGV volume and PGV volume, and PGV perceived usefulness strengthens the positive effect of PGV volume on box office revenue. These findings reveal a sequential pathway through which user-generated social attention is transformed into professional market persuasion, offering theoretical and managerial implications for video-based interactive marketing. Full article

The accurate evaluation of gastrointestinal (GI) diseases such as celiac disease (CeD) relies on the assessment of villous architecture, yet progress in imaging-based diagnostics, particularly video capsule endoscopy (VCE), is constrained by the absence of anatomically realistic and reproducible physical models of the intestinal mucosa. Existing benchtop phantoms typically reproduce gross luminal curvature but fail to capture the sub-millimeter villous microstructure, the optical scattering behavior, and the luminal folding of native mucosa that together shape its endoscopic appearance. We developed a modular fabrication framework for an anatomically realistic small intestinal phantom with controlled villous microstructure. High-resolution drop-on-demand photopolymer material jetting was used to print discrete patches of villous-like micropillar arrays with tunable height, diameter, and spacing parameterized from histological data spanning Marsh 0 to 3c classifications. The printed patches were then dyed for mucosal-color realism, bonded onto a polyester–spandex substrate, rolled into a continuous tube, and shaped with adjustable retainer rings to introduce luminal folds. Optical microscopy confirmed dimensional fidelity within ±10% of design values with patch-to-patch variation below 7%, and VCE imaging of healthy and atrophic configurations achieved structural similarity (SSIM) values of 0.625 and 0.761 against clinical mucosal imagery. This reproducible platform supports VCE device validation, imaging dataset generation, and clinician training in gastrointestinal imaging. Full article

Background: Poor adherence to home exercise programs is a persistent challenge in physical rehabilitation and beyond. Data suggest that follow-up telehealth visits featuring behavior change techniques can improve adherence. However, the optimal frequency of behavioral support for effective self-management of technology-enabled home exercise programs remains unclear. This paper investigates how adherence to home exercise varies with the number of follow-up visits holding the type of exercise constant and measuring adherence objectively. Methods: A secondary analysis of adherence was conducted on participants from the VIGOROUS five-site, single-blind, and randomized controlled trial. Chronic (>6 months) community-resident stroke survivors with mild-to-moderate upper extremity hemiparesis (n = 111) were assigned 15 h of asynchronous video game-based home exercise over 3 weeks. Participants received behavioral support during (a) one single in-person session, (b) four in-person sessions, or (c) four in-person sessions plus six remote teleconsultations. Each consultation incorporated standardized behavior change techniques—contracting, feedback, self-monitoring, action planning, and guided problem solving—to promote increased paretic arm use during daily activities. The primary outcome was adherence (minutes exercising), objectively captured by the gaming system for 87 participants. Results: Six telehealth visits with remote therapeutic monitoring (RTM) increased home exercise completion by 3.3 h [95% CI: 0.008, 6.60]; each remote or in-person consultation was associated with an estimated 34 min of additional asynchronous home exercise. Conclusions: RTM, which does not require travel to a healthcare facility, is a time-efficient modality to enhance therapeutic engagement. Behaviorally focused telehealth visits boost stroke survivors’ adherence to a structured home exercise program. Full article

Human action recognition relies heavily on large-scale annotated video datasets, which are costly and time-consuming to curate, while AI-generated videos offer a promising alternative data source, their effectiveness for training action recognition models remains insufficiently explored. This study evaluates AI-generated videos for action recognition across convolutional and transformer-based architectures using real, synthetic, and hybrid datasets. To ensure generative diversity and consistency, a structured prompt engineering pipeline combining action descriptions, environmental contexts, and camera viewpoints was developed. Synthetic datasets were generated using the Grok Imagine and Meta AI Vibes video generation models and paired with a 15-class subset of the Human Motion Database 51 (HMDB51) to construct the Generative Synthetic Human Action Recognition Dataset (GenSynth-HARD). To mitigate domain shift arising from discrepancies between real and AI-generated videos, a Conditional Domain Adversarial Network (CDAN) with a dynamically scaled Gradient Reversal Layer (GRL) was integrated for domain feature alignment. The best-performing hybrid model achieved a Top-1 accuracy of 79.92% on the HMDB51 subset, demonstrating that incorporating synthetic videos effectively supports model performance while significantly reducing annotation overhead. Full article

The integration of the Internet into clinical practice has facilitated the implementation of digital mental health interventions, including eTherapy. In this context, the present study examined factors associated with Therapist Self-Efficacy during synchronous video-based eTherapy, specifically focusing on Self-Esteem, General Self-Efficacy, and Insight Orientation while controlling for Years of Clinical Practice. A sample of 290 mental health professionals (39% psychologists; 61% psychotherapists) completed a survey including the Therapist Self-Efficacy Scale, General Self-Efficacy Scale, Rosenberg Self-Esteem Scale, and Insight Orientation Scale. A path analysis was performed to analyse the collected data. Results showed a significant total mediation model. Specifically, significant and positive total effects in the relationships of Self-Esteem and General Self-Efficacy with Professional Self-Efficacy emerged. Furthermore, Insight Orientation fully mediated these associations. Years of Clinical Practice was not significantly associated with Therapist Self-Efficacy. These findings may inform the development of tailored eTherapy training for mental health professionals. Full article

Care settings need short, accessible digital activities that encourage older adults to participate while generating process data during ordinary interaction. We developed a game-based platform with four activities: reflective choice, guided breathing, reminiscence, and artistic expression. Session video, interaction logs, and field notes were organized in a context-preserving multimodal framework. This exploratory seven-module field study included 12 older adults aged 65–90 years in a hospital-affiliated care setting. Ten participants completed all seven scheduled modules, and 81 of 84 planned module sessions were completed; some participants completed multiple modules on the same calendar day. Seventy-two videos were analyzable, representing 88.9% of completed module sessions (72/81). Across 251 activity selections, reminiscence was selected most often; mouse control and navigation were the main operating difficulties. Two raters independently annotated forty 10 s field-video clips. Target-face tracking was stable or basically stable in 90% of clips. Gaze-deviation detection achieved 87.5% accuracy and a macro-F1 of 0.871, while macro-expression classification achieved 67.5% accuracy and a macro-F1 of 0.610. The findings demonstrate a context-preserving field method for structured process records. The algorithms are auxiliary tools, and facial-expression outputs should not be interpreted as direct measures of emotion, cognition, or clinical status. Full article

In early childhood education (ECE), eye-tracking research has primarily investigated in-service teachers’ visual gaze and reflective practices in pedagogical settings. However, little is known about how pre-service ECE teachers’ visual information-processing is oriented by their pedagogical goals, particularly in authentic play-based contexts. This paper addresses this gap by examining how pre-service ECE teachers adapt their professional vision during children’s play. The study involved two pre-service ECE teachers with work experience. The data consisted of video recordings and mobile eye-tracking data collected during authentic play situations involving children aged 3–5, participants’ written pedagogical plans, and cued retrospective recall interviews with mobile eye-tracking recordings as interview stimuli. Integrating these data sources enables a process-oriented analysis of professional vision by linking observable gaze patterns with participants’ retrospective accounts of what they attended to and why. The data demonstrate that professional vision is a goal-oriented process in which pre-set goals guide pre-service teachers’ gaze allocation, shaping their interpretations of children’s play in pedagogically relevant ways. The analyses also reveal instances of unaddressed pedagogical opportunities. These instances restrict teachers’ ability to reorient their attention in response to children’s initiatives. The study conceptualizes visual information-processing in play-based contexts as a dynamic relationship between pre-set pedagogical goals and ad hoc adaptation, thereby offering insights into emerging aspects of developing adaptive expertise. Full article

Today, data are no longer confined to numerical values arranged in row-by-column matrices or stored neatly within relational databases. One of the defining characteristics of big data is its high variety, encompassing unstructured and multimodal forms such as text, audio, images, and video. These data types dominate contemporary domains including social media, digital humanities, biomedical research, education, and surveillance systems. Yet these data types remain difficult to manage and analyze using traditional data management architectures. To cope with this shift, modern data management systems must move beyond schema-driven designs and incorporate multimodal artificial intelligence capable of understanding, integrating, and reasoning across heterogeneous data modalities. This article examines how multimodal AI, in particular large multimodal foundation models, can be leveraged to support the ingestion, representation, organization, and analysis of unstructured data. It discusses emerging multimodal data management frameworks, outlines a conceptual pipeline for multimodal data analysis, and highlights key challenges related to scalability, interpretability, and governance. By situating multimodal AI at the core of data management, this work argues that effective data analysis in the era of big data requires systems that treat meaning, context, and cross-modal relationships as first-class computational objects rather than afterthoughts. Full article

Passenger Car Equivalent (PCE) factors are widely used to convert heterogeneous traffic streams into equivalent homogeneous flow rates for the design and analysis of roads and intersections. In developing countries, mixed traffic conditions differ substantially from those in developed contexts due to variations in vehicle composition, operating characteristics, roadway parameters, and environmental conditions. Consequently, PCE values are highly context-specific and require periodic updates to accurately represent prevailing traffic conditions. However, such updates are often infrequent because conventional PCE estimation relies on extensive field data collection through time-consuming and costly traffic surveys, as well as the availability of experienced experts to conduct and validate the analyses. In Sri Lanka, the currently adopted PCE factors are more than two decades old and no longer reflect existing traffic conditions. Although several recent studies have estimated PCE values for mid-block roadway sections of various facility types (e.g., four-lane roads, two-lane roads, and freeways), no study has comprehensively addressed intersections, which are critical for signal timing and geometric design. This study aims to develop a systematic methodology for estimating intersection-specific PCE factors using drone-based video data. Traffic data were collected at selected intersections using an unmanned aerial vehicle to obtain an accurate bird’s-eye view of vehicle movements. The methodology compares the area occupancy of different vehicle categories under varying traffic compositions with that of a passenger-car-only traffic stream operating at the same average speed. Using the extracted traffic parameters, the basic headway method was applied to establish a framework for calculating PCE factors. PCE values were estimated for ten vehicle categories, and the results reveal significant deviations, particularly for three-wheelers, motorcycles, and commercial vehicles, when compared with values currently in use. A high-level comparison with studies from other developing countries in the South Asian region indicates notable differences in vehicle impacts at signalized intersections in Sri Lanka. Furthermore, the proposed methodology provides a practical, economical, and less labour-intensive approach for estimating PCE factors, enabling more frequent updates without requiring extensive field surveys or specialized expertise. Because it relies on a straightforward headway-based framework and drone-derived traffic data, the methodology can be readily adapted to different roadway facilities, including highways, rural roads, and intersections, making it suitable for application across diverse geographical regions. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is caused by excessive fat accumulation in the liver (steatosis) and affects approximately 38% of adults globally. MASLD may progress from simple steatosis to fibrosis and cirrhosis and is closely related to other cardio-metabolic conditions (e.g., obesity and type 2 diabetes), posing a risk factor for cardiovascular disease. Currently, lifestyle modification and weight reduction remain the main initial treatment options. Existing data suggest that there is low awareness among patients regarding MASLD diagnosis and its subsequent management. Therefore, this study aimed to develop a rich understanding of the lived experiences, concerns, and support needs of adults with MASLD. Methods: A qualitative design was applied, utilizing semi-structured interviews. Adults living with MASLD were invited to talk about their diagnosis and discuss their lived experiences. Participants were interviewed by telephone or video call, and each interview was transcribed and analyzed with a reflexive thematic analysis approach. Results: Twenty-five adults with MASLD (age range: 24–79 years; 40% men) were interviewed. The emergent key themes related to communication and emotions at diagnosis; independently seeking further information; lived experiences post-diagnosis; support needs; and future concerns. Many participants reported receiving the diagnosis incidentally, and a number of issues were raised regarding lack of clarity at the point of diagnosis. Additional concerns included information obtainable via the internet, symptoms, social relationships, stigma, and lifestyle modification. Future anxieties related mainly to fears of disease progression, while support needs were predominantly focused on information and follow-up. Conclusions: The concerns and support needs identified by this study highlight key issues/themes that should inform education and support initiatives by relevant healthcare services aiming to improve the lived experiences and holistic management of adults with MASLD. Full article

Background/Objectives: Motor abnormalities are frequently observed in schizophrenia, but accessible methods for objective gait quantification remain limited. This exploratory controlled-setting study examined whether markerless smartphone-based video analysis combined with interpretable machine learning could quantify gait-related motor phenotypes in individuals with schizophrenia. Methods: Gait videos were collected from 100 individuals with schizophrenia and 35 healthy controls using a single-site, single-device, standardized recording setup. MediaPipe Pose was used to extract skeletal landmarks and derive 12 image-plane spatiotemporal and estimated two-dimensional knee-kinematic gait features. After temporal segmentation and quality control, 404 usable gait segments derived from 135 participants were analyzed as repeated segment-level observations. Decision Tree and Support Vector Machine models were applied for exploratory segment-level group-separation analysis using segment-wise 15-fold cross-validation after the full post-quality-control dataset had been balanced before fold allocation. Results: Several extracted gait features differed between groups, particularly image-plane ankle displacement, mean step displacement, displacement velocity, step characteristics, and knee-joint motion. In the Decision Tree model, image-plane ankle displacement served as the primary root node, indicating its central role in internal segment-level group separation. However, the healthy control group was substantially younger and not age-matched. In addition, the segment-level statistical comparisons did not account for within-participant clustering. Accordingly, the reported p values and confidence intervals may overstate statistical precision. Separately, segment-wise cross-validation allowed segments from the same participant to occur across folds and resampling was performed before fold partitioning. Because oversampling was performed with replacement, duplicated segment instances could also occur across training and validation folds. Consequently, the statistical findings should be interpreted as exploratory segment-level patterns rather than participant-level inference, and the machine-learning performance estimates should be regarded only as potentially optimistic apparent internal segment-level results and should not be regarded as evidence of participant-level generalization, diagnostic validity, screening accuracy, or clinical applicability. Conclusions: Markerless video-based gait analysis with interpretable machine learning may provide a feasible research-support approach for quantifying gait-related motor phenotypes in individuals with schizophrenia. These findings should not be interpreted as evidence for participant-level clinical classification, diagnostic or screening validity, clinical utility, or readiness for deployment, or as proof of cross-device or cross-environment reproducibility. Future studies require matched controls, psychiatric comparison groups, subject-wise validation, external datasets, calibrated gait measures, systematic cross-configuration reproducibility testing, and privacy-preserving data governance. Full article