Search Results (63)

The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article

Background/Objectives: Early and accurate prediction of stroke severity is crucial for optimizing guided therapeutic decisions and improving outcomes. This study investigates the predictive value of lesion size and functional connectivity for neurological deficits, assessed by the National Institutes of Health Stroke Scale (NIHSS score), in patients with acute or subacute subcortical ischemic stroke. Methods: Forty-four patients (mean age: 68.11 years, 23 male, and admission NIHSS score 4.30 points) underwent high-resolution anatomical and resting-state functional Magnetic Resonance Imaging (rs-fMRI) within seven days of stroke onset. Lesion size was volumetrically quantified, while functional connectivity within the motor, default mode, and frontoparietal networks was analyzed using seed-based correlation methods. Multiple linear regression and cross-validation were applied to develop predictive models for stroke severity. Results: Our results showed that lesion size explained 48% of the variance in NIHSS scores (R² = 0.48, cross-validated R² = 0.49). Functional connectivity metrics alone were less predictive but enhanced model performance when combined with lesion size (achieving an R² = 0.71, cross-validated R² = 0.73). Additionally, left hemisphere connectivity features were particularly informative, as models based on left-hemispheric connectivity outperformed those using right-hemispheric or bilateral predictors. This suggests that the inclusion of contralateral hemisphere data did not enhance, and in some configurations, slightly reduced, model performance—potentially due to lateralized functional organization and lesion distribution in our cohort. Conclusions: The findings highlight lesion size as a reliable early marker of stroke severity and underscore the complementary value of functional connectivity analysis. Integrating rs-fMRI into clinical stroke imaging protocols offers a potential approach for refining prognostic models. Future research efforts should prioritize establishing this approach in larger cohorts and analyzing additional biomarkers to improve predictive models, advancing personalized therapeutic strategies for stroke management. Full article

Background/Objectives: Students with specific learning disorders (SLDs) are at increased risk for emotional and academic difficulties. While teacher characteristics can influence student outcomes, few studies have examined the role of teacher personality in supporting students with SLDs. This study investigated whether teacher personality traits predicted student emotional well-being and academic achievement in a school-based intervention context. Methods: Participants were 64 students with SLDs (Mage = 13.28) nested within 21 teachers. Students completed measures of emotional well-being at baseline and post-intervention, and grade point average (GPA) was obtained from school records at the end of the school year. Teachers completed the Big Five Inventory mid-intervention. Two-level multilevel models were conducted in Mplus using maximum likelihood estimation with robust standard errors (MLR). The models controlled for student and teacher demographics, baseline emotional well-being, and the intervention group. Missing data were addressed using full information maximum likelihood (FIML). Results: Teacher female sex, higher neuroticism, and lower teaching experience were associated with higher student emotional well-being post-intervention. Follow-up analyses confirmed that teacher sex, neuroticism, and conscientiousness each explained substantial between-teacher variance. In the GPA model, student sex and teacher openness were significant predictors, with female students and students taught by more open teachers earning higher GPAs. Conclusions: Teacher personality traits, specifically neuroticism, conscientiousness, and openness, were associated with emotional and academic outcomes among students with SLDs. The findings highlight the importance of considering teacher characteristics in designing school-based interventions to support the development of learners with SLDs or other neurodevelopmental disorders. Full article

Background: The gut microbiome plays a pivotal role in shaping systemic immunity and modulating anti-tumor responses. Preclinical and clinical studies have shown that higher gut microbial diversity and the presence of specific commensal taxa correlate with improved responses to immune checkpoint inhibitors (ICI) in melanoma. Conversely, broad-spectrum antibiotics can induce dysbiosis, reducing T cell activation and cytokine production, and have been linked to diminished ICI efficacy in several cancer types. Methods: We conducted a systematic review and meta-analysis of seven retrospective cohorts (total n = 5213) comparing overall survival in cutaneous melanoma (CM) patients who did or did not receive systemic antibiotics within six weeks before ICI initiation. From each study, we extracted hazard ratios (HRs) for death, antibiotic-to-ICI interval, ICI regimen (PD-1 monotherapy vs. PD-1 + CTLA-4 combination), cohort size, and country. Pooled log-HRs were estimated under fixed-effect and random-effects (REML) models. Statistical heterogeneity was quantified by Cochran’s Q and I² statistics, and τ². We performed leave-one-out sensitivity analyses, generated a Baujat plot to identify influential studies, applied trim-and-fill to assess publication bias, and ran meta-regressions for regimen, antibiotic timing, sample size, and geography. Results: Under the fixed-effect model, antibiotic exposure corresponded to a pooled HR of 1.26 (95% CI 1.13–1.41; p < 0.001). The random-effects model yielded a pooled HR of 1.55 (95% CI 1.21–1.98; p = 0.0005) with substantial heterogeneity (Q = 25.1; I² = 76%). Prediction intervals (0.78–3.06) underscored between-study variability. Leave-one-out analyses produced HRs from 1.50 to 1.75, confirming robustness, and the Baujat plot highlighted two cohorts as primary heterogeneity drivers. Trim-and-fill adjusted the HR to 1.46 (95% CI 1.08–1.97). In subgroup analyses, combination therapy studies (k = 4) showed a pooled HR of ~1.9 (I² = 58%) versus ~1.3 (I² = 79%) for monotherapy. Meta-regression attributed the largest variance to the regimen (R² = 32%; β(monotherapy) = −0.35; p = 0.13). Conclusions: Pre-ICI antibiotic use in CM is consistently associated with a 26–55% increase in mortality risk, particularly with PD-1 + CTLA-4 combinations, reinforcing the mechanistic link between microbiome integrity and ICI success. Looking ahead, integrating prospective microbiome profiling into clinical trials will be critical to personalize ICI therapy, clarify causality, and identify microbial biomarkers for optimal treatment selection. Prospective, microbiome-integrated trials promise to refine melanoma immunotherapy by tailoring antibiotic stewardship and microbial interventions to enhance patient outcomes. Full article

In this study, we examined the role of intellectual humility (IH) as an antecedent of individual attitude toward sustainable tourism viewed from the lens of personality trait theory, virtue ethics theory, and regenerative tourism principles within a stakeholder framework. Data were collected via Qualtrics in an online survey of 233 adults in the United States. A series of confirmatory factor analyses (CFA) were applied to the data to test the measurement model. In addition, a bifactor CFA was found to have acceptable fit and appropriate in controlling for common method variance. A series of covariance-based structural equations models (SEMs) was estimated to test the hypothesized model while controlling for common method variance in addition to individual age and gender. Using the chi-square difference test for nested model comparison, we found that intellectual humility was a significant antecedent of the negative ecological impact of tourism (β = 0.14, p < 0.01) while its relationships with economic and social impacts of travel became non-significant after controlling for common method variance. Pro-social tendency, operationalized as HEXACO Honesty–Humility, was also a significant antecedent of the negative ecological impact (β = 0.17) and positive economic impact (β = −0.34) of tourism, after controlling for common method variance. Despite its limitations due to its cross-sectional design and use of self-report data in the U.S., this study was novel in introducing intellectual humility as an important virtue to be cultivated at the individual level to achieve a holistic approach to sustainable tourism, especially in shaping destination choices. In addition, the study highlights the need to detect common method variance in self-report data via bifactor CFA to avoid erroneous reporting of significant findings, hampering our collective research efforts to address climate change and its impact. Full article

Vietnam’s tourism sector has experienced rapid expansion, yielding significant economic gains, but also escalating environmental and socio-cultural challenges. This necessitates a shift toward sustainable tourism practices. This study develops and tests an integrative model combining the Theory of Planned Behavior (TPB) and Value–Belief–Norm (VBN) theory to investigate the Sustainable Consumption Behavior (SCB) of domestic travelers in Vietnam. Grounded in the country’s collectivist culture—characterized by a strong emphasis on communal values and social harmony—the research examines how rational decision-making and normative pressures jointly drive eco-friendly travel behaviors. Data were collected from 549 Vietnamese tourists through stratified sampling across three major regions, and were analyzed through a robust multi-step methodology, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and structural equation modeling (SEM), supplemented by regression analysis. The SEM results indicate that the proposed model accounts for 60.8% of the variance in SCB, underscoring its explanatory power. Within the TPB framework, Attitude, Subjective Norms, and Perceived Behavioral Control significantly predict Behavioral Intention, which, in turn, mediates their influence on SCB. In the VBN component, Environmental Awareness shapes Altruistic and Biospheric Values, fostering the New Environmental Paradigm and activating Personal Norms, which exert a direct effect on SCB. This direct influence of Personal Norms, bypassing Behavioral Intention, highlights the role of implicit moral obligations in Vietnam’s collectivist cultural context and challenges Western-centric behavioral models. Beyond these frameworks, Destination Attributes and Consumer Innovativeness emerge as critical direct predictors of SCB, with Destination Attributes showing the strongest effect. Mediation analyses confirm Behavioral Intention’s central role, while moderation tests reveal that gender and travel experience modulate TPB-based relationships, suggesting the value of targeted interventions. Theoretically, this study advances TPB–VBN integration by applying it to a collectivist setting, revealing how cultural norms amplify normative influences on sustainable behavior. Practically, it advocates for strategies such as community-driven initiatives leveraging Personal Norms, educational campaigns to enhance Perceived Behavioral Control, and sustainable destination management initiatives in locations like Da Nang and Ha Long Bay. While the cross-sectional design limits causal claims, the findings provide a foundation for future longitudinal studies and cross-cultural comparisons, particularly in other collectivist societies, potentially incorporating digital engagement and policy incentives as novel factors. Full article

Nocturnal monitoring of continuous, cuffless blood pressure (BP) can unleash a whole new world for the prognostication of cardiovascular and other diseases due to its strong predictive capability. Nevertheless, the lack of an accurate and reliable method, primarily due to confounding variables, has prevented its widespread clinical adoption. Herein, we demonstrate how optimized machine learning using the Catch-22 features, when applied to the photoplethysmogram waveform and personalized with direct BP data through transfer learning, can accurately estimate systolic and diastolic BP. After training with a hemodynamically compromised VitalDB “calibration-free” dataset (n = 1293), the systolic and diastolic BP tested on a distinct VitalDB dataset that met AAMI criteria (n = 116) had acceptable error biases of −1.85 mm Hg and 0.11 mm Hg, respectively [within the 5 mm Hg IEC/ANSI/AAMI 80601-2-30, 2018 standard], but standard deviation (SD) errors of 19.55 mm Hg and 11.55 mm Hg, respectively [exceeding the stipulated 8 mm Hg limit]. However, personalization using an initial calibration data segment and subsequent use of transfer learning to fine-tune the pretrained model produced acceptable mean (−1.31 mm Hg and 0.10 mm Hg) and SD (7.91 mm Hg and 4.59 mm Hg) errors for systolic and diastolic BP, respectively. Levene’s test for variance found that the personalization method significantly outperformed (p < 0.05) the calibration-free method, but there was no difference between three machine learning methods. Optimized multimodal Catch-22 features, coupled with personalization, demonstrate great promise in the clinical adoption of continuous, cuffless blood pressure estimation in applications such as nocturnal BP monitoring. Full article

Background/Objectives: Adolescence is a critical period for mental health, with depression increasing rapidly and often leading to lifelong consequences. In recent decades, the prevalence of elevated depressive symptoms among adolescents has steadily risen, making it a significant public health concern. While research supports the benefits of mindfulness-based practices in reducing adolescent depressive symptoms, the role of trait mindfulness remains underexplored. Although some studies suggest a link between trait mindfulness and lower depressive symptomatology, a longitudinal perspective could provide deeper insights into this relationship. Given adolescents’ heightened vulnerability to mental health issues, understanding the potential causal link between trait mindfulness and depression is crucial for both prevention and intervention efforts. Methods: This study examines the relationship between mindfulness and depression in a three-wave longitudinal study of 1618 secondary school students (Males: N = 671, M = 16.4 years, SD = 0.60; Females: N = 947, M = 16.3 years, SD = 0.65) using a random intercept cross-lagged panel model. Results: Findings indicate that mindfulness and depression share a substantial proportion of variance (r = 0.48) at the stable trait level, suggesting that sustained attentional focus, a hallmark of mindfulness, is consistently associated with fewer depressive symptoms. At the within-person level, momentary deviations from stable mindfulness levels in the first and second waves were linked to lower depressive symptoms in subsequent waves (β = −0.21, p = 0.016; β = −0.44, p = 0.03, respectively). These findings suggest that even temporary increases in mindfulness may provide additional protection against depression. Conclusions: Overall, the results suggest that trait mindfulness is associated with a reduced risk of developing depressive symptoms at both the between-person and within-person levels. Specifically, adolescents with higher stable levels of mindfulness tend to report fewer depressive symptoms over time, and even momentary increases in mindfulness beyond an individual’s typical level are linked to reductions in subsequent depressive symptoms. These findings highlight the potential of mindfulness-based interventions in mitigating adolescent depression and underscore the importance of cultivating mindfulness as a protective factor during this critical stage of development. Full article

Variations within the serotonin transporter gene, SLC6A4 (solute carrier family 6 member 4), particularly the 5-HTTLPR (serotonin-transporter-linked promoter region), have been extensively studied in relation to behavioral and psychological traits. The aim of our study is to examine the relationship between the 5-HTTLPR polymorphism located in the SLC6A4 gene and personality traits, as assessed using the NEO-FFI (NEO Five Factor Inventory). The MANOVA model demonstrated a significant overall association, accounting for approximately 8% of the variance in the data (Wilk’s λ = 0.847, F_10,342 = 2.979, p = 0.0013, η² = 0.08). Subsequent ANOVAs revealed statistically significant 5-HTTLPR polymorphism associations with the Neuroticism (p = 0.0018, R² = 0.070), Openness (p = 0.0364, R² = 0.037), and Conscientiousness (p = 0.0020, R² = 0.068) dimensions. The post-hoc analysis revealed that individuals with the LL genotype obtained significantly lower Neuroticism scores compared to the S/S (p = 0.0011) and SL genotype (p = 0.0086) carriers. Similarly, individuals with the L/L genotype had lower Openness scores compared to those with SS genotype (p = 0.0107). LL and SL genotype carriers had higher Conscientiousness scores compared to those with the SS genotype (p = 0.0004 and p = 0.0109, respectively). In conclusion, our study provides further data regarding the implications of 5-HTTLPR polymorphism in the complex genetic architecture of human personality. The observed associations with Neuroticism, Openness, and Conscientiousness, while modest in effect size, contribute to our understanding of how genetic variation at the SLC6A4 locus may subtly shape individual personality differences. Full article

Background: Motor impairments following stroke contribute to deficits in functional mobility. Traditionally, these impairments are quantified by mean-level motor performance. However, this mean-level approach neglects the well-established fact that motor performance becomes highly variable in aging and disease. Increased intra-individual variability (IIV) in behavior predicts functional decline in neurological disorders. Despite this, the impact of stroke on IIV in motor performance and its influence on functional mobility has not been investigated. This study aimed to (1) quantify the impact of stroke on IIV in motor performance, and (2) determine the contribution of IIV and mean motor performance to functional mobility. Methods: Twenty stroke survivors and 20 age-matched controls performed a goal-directed ankle movement task over 30 trials. We measured average accuracy (mean endpoint error) and IIV (within-person SD of endpoint error). Functional mobility was assessed with postural control (sway area during quiet standing) and braking response time in a driving simulator. Results: Stroke participants showed a higher mean (p = 0.04) and greater IIV (p = 0.016) in endpoint error than controls. Sway area did not differ between groups (p = 0.24), but stroke survivors had increased braking response time (p = 0.016). In stroke survivors, IIV significantly predicted sway area (R² = 0.33, p = 0.008) and braking response time (R² = 0.27, p = 0.02), and mean error did not account for any additional variance. Conclusions: Stroke reduces the trial-to-trial consistency of executing motor tasks with precision. IIV in motor performance predicts postural balance and braking response time and can potentially serve as an indicator of increased vulnerability and an important target for stroke rehabilitation. Full article

Background and Objectives: Societies are obliged to ensure that children in need of protection grow up psychologically, socially, and physically healthy. To achieve that, various practices have been developed. One of these practices is the use of children’s homes sites. Accordingly, this study examined the relationships between the life satisfaction, attachment styles, and self-compassion levels of children in need of protection staying in children’s homes sites. It aimed to determine to what extent variables such as attachment style and self-compassion predict life satisfaction. In addition, whether life satisfaction differs according to gender, age, and the status of having a visitor was examined. Methods: The study sample consisted of 121 children between the ages of 8 and 14 who were staying in children’s homes sites in Istanbul between March and May 2022. The Satisfaction with Life Scale, the short form of the Self-Compassion Scale, the Three-Dimensional Attachment Style Scale, and the Personal Information Form were used with the participants. The researchers used the independent samples t-test, multiple linear regression analysis, Pearson product moment coefficient, and one-way analysis of variance (ANOVA) during the analysis. Results: The findings showed that there was a significant positive relationship between secure attachment style and life satisfaction, anxious–ambivalent attachment, and self-compassion. Also, among the demographic variables, being visited by relatives was found to predict life satisfaction, while there was no relationship between life satisfaction and age and gender. Conclusions: The study findings were discussed within the framework of the literature, and suggestions based on the findings were presented. Full article

This study is based on the development of a design focused on underlining what artificial intelligence can achieve to bring value to learning quality especially when implementing active methodologies, such as project-based learning (PBL). This study analyzed the perceptions of AI-integrated PBL versus non-AI-integrated PBL among teachers in primary, secondary, and higher education. Conducted with a sample of teachers (n = 300), this study investigated perceived effectiveness, the AI-powered personalization of learning, and motivation. A Student’s t-test, as well as normality, homogeneity of variance, and Cohen’s d tests, revealed that AI-enhanced PBL is rated significantly higher than regular PBL without AI, with a Cohen’s d effect size of 1.30, signifying a large impact. These findings underpin the development of an optimized AI-driven PBL model, particularly within both the prototype production and evaluation phases, providing greater autonomy, responsive feedback, and adaptive personalization, all towards advancing a more effective AI-supported pedagogical model of teaching. Full article

Background/Objectives: Persons with multiple sclerosis (MS) are interested in diet as a second-line approach for disease management. This study examined potential variables that correlate with inaccuracy of self-reported energy intake (EI) in adults with MS. Methods: Twenty-eight participants completed two assessment appointments within a 14-day period that included a standard doubly labeled water (DLW) protocol for estimating total energy expenditure (TEE). The participants reported their EI using the Automated Self-Administered 24 h (ASA24) Dietary Assessment Tool. The primary variables of interest for explaining the discrepancy between TEE and ASA24 EI (i.e., inaccuracy) included cognition (processing speed, visuospatial memory, and verbal memory), hydration status (total body water), and device-measured physical activity. Pearson’s correlations assessed the association between absolute and percent inaccuracy in reporting of EI with outcomes of interest, followed by linear regression analyses for identifying independent correlates. Results: California Verbal Learning Test—Second Edition (CVLT-II) z-scores and light physical activity (LPA) were significantly associated with mean absolute difference in EI (r = –0.53 and r = 0.46, respectively). CVLT-II z-scores and LPA were the only variables significantly associated with mean percent difference in EI (r = –0.48 and r = 0.42, respectively). The regression analyses indicated that both CVLT-II and LPA significantly explained variance in mean absolute difference in EI, and only CVLT-II explained variance for percent difference in EI. Conclusions: The results from this study indicate that verbal learning and memory and LPA are associated with inaccuracy of self-reported EI in adults with MS. This may guide timely research identifying appropriate protocols for assessment of diet in MS. Full article

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology. Full article

This study investigates the sustainability consciousness and environmental behaviors of sports science students, focusing on demographic differences. A total of 440 voluntary students (213 females and 227 males) from the Faculty of Sports Sciences participated, selected through simple random sampling. Data collection tools included a researcher-designed personal information form, the Sustainable Consciousness Scale, and the Environmental Behavior Scale. Data analysis was performed using SPSS 22, employing descriptive statistics to summarize the data. Normality tests were conducted to determine the suitability of parametric analyses, followed by t-tests and one-way analysis of variance (ANOVA) tests. Pearson correlation and regression analyses were also utilized. The findings revealed no significant differences in sustainability consciousness and environmental behaviors based on variables such as active sports participation or sports duration. However, significant differences were identified based on gender, age, and academic achievement. Additionally, a strong positive relationship was observed between sustainability consciousness and environmental behaviors. The findings underscore the need for personalized educational strategies that consider demographic differences in fostering pro-environmental behaviors. These insights have practical implications for the development of targeted curricula and policies aimed at enhancing sustainability consciousness in higher education, particularly within sports sciences. Tailored interventions for different demographic groups could improve the effectiveness of sustainability education and encourage greater environmental responsibility among future sports professionals. Full article