Search Results (544)

Background/Objectives: Depression is a mood disorder characterized by persistent sadness and loss of interest. Pharmacy students exhibit a relatively high prevalence of depressive symptoms, which may negatively impact quality of life, academic performance, and, in severe cases, lead to suicidal ideation. Given the increasing trend of medication use among young adults, we hypothesized that this behavior may be associated with depressive symptoms, potentially reflecting attempts to manage concurrent physical symptoms or reduced psychological well-being. Therefore, the objective of this study was to assess the association between depressive symptoms and medication use among pharmacy students. Methods: A cross-sectional study was conducted among students enrolled in pharmacy-related programs from University Center for Exact Sciences and Engineering (CUCEI), University of Guadalajara. Participants completed a personalized questionnaire assessing sociodemographic variables, medication use, comorbid conditions, and depressive symptoms using the Patient Health Questionnaire-9. Descriptive statistics were used to summarize participant characteristics. Categorical variables were reported as frequencies and percentages and compared using the chi-square test. Continuous variables were summarized as means and standard deviations and compared using Student t-test. To evaluate factors associated with moderate-to-severe depressive symptoms, logistic regression and multivariable linear regression analyses were performed. Results: A total of 365 students completed our personalized questionnaire; nearly half of the sample (47.3%) presented moderate-to-severe depressive symptoms. Multivariable analyses showed that insufficient sleep, academic stress, psychological support, and the number of medications used simultaneously were significantly associated with depressive symptoms. Logistic regression identified being in a relationship and receiving psychological support for at least one year as protective factors, while employment, insufficient sleep, academic stress, and a greater number of concomitant medications were associated with increased odds of moderate-to-severe depressive symptoms. Conclusions: A modest association was observed between self-reported medication use and moderate-to-severe depressive symptoms among pharmacy students. These findings suggest that medication use patterns may warrant further investigation as a potential marker of depressive symptoms in pharmacy students. Future longitudinal studies are needed to clarify the nature and direction of this association and to determine whether medication use could contribute to the identification of students at increased risk of depression. Full article

Engineering education prepares graduates to face complex environmental and societal challenges. This involves the intersection of sustainability and innovation. Integrating these agendas is therefore necessary, and this involves identifying specific elements that have not yet been explored. To examine this relationship, a systematic literature review was conducted using an adapted PRISMA 2020 approach appropriate for a bibliometric and thematic systematic review, through four research questions related to knowledge production, pedagogical methods, innovation outcomes, and reported results. The PRISMA phases were adopted using the SCOPUS and ERIC databases. This yielded three clusters: innovation, sustainability, and engineering education. Student-centered pedagogies have also been identified as an explored opportunity to enhance innovation skills aligned with sustainability objectives. However, this incorporation involves many elements to explore, including the connection between innovation outcomes and sustainability impact. This context involves both development and the relationships among individuals, institutions, and ecosystems. This requires managing diverse visions, languages, and cultures, which highlights several challenges: long-term impacts, mindset development, contextual influences, pedagogical strategies, research–practice alignment, stakeholder communication, and faculty preparation. Overall, the findings show progress but reveal challenges across approaches and contexts. This is because sustainability-driven innovation in engineering education requires coordinated curricular, institutional, and ecosystem-oriented strategies to support learning and strengthen contributions to sustainable futures. Full article

Students with disabilities, particularly those needing additional support or intervention to manage emotions and behaviors, build healthy relationships, and navigate social and academic demands, face heightened risks of high school pushout that can be traced back to their transition into high school. Project Elevate (PE) is a multi-component intervention that strategically invests in early coordinated student, family, and school supports to prevent barriers associated with high school pushout, such as a lack of continuity of effective services across school sites. This mixed-methods pilot study examined the implementation of PE with three 8th-grade students and their parents during their last term in middle school. This study includes quantitative pre–post descriptive analyses of multi-informant reports of students’ social, emotional, and behavioral skills, as well as descriptive analyses of weekly teacher- and parent-reported behavior and student attendance. Qualitative analysis using the Framework Method was applied to student and parent interviews and open-ended responses on a satisfaction questionnaire to understand their experience receiving PE support. Session case notes were also used as contextual data to describe implementation processes and contextualize findings. Results indicated improvements in student attendance and reductions in home-based behavioral concerns, with mixed findings across school-based outcomes. Students and parents reported high satisfaction with the intervention, highlighting the value of individualized support, goal setting, and strengthened communication with schools. Findings from this intervention development pilot study provide preliminary evidence regarding the implementation and perceived value of PE. Results also highlight the importance of culturally responsive, relationship-centered practices that affirm student strengths and support access to educational opportunities. Further investigation of PE in larger studies is warranted. Full article

Learning management systems (LMSs) are increasingly recognized as tools for promoting sustainable education, yet the psychological mechanisms linking LMS usability to student motivation remain underexplored. This three-study research develops and validates the Moodle-based BirDeHa Usability Scale (BirDeHa-US) and examines academic locus of control as a mediator between LMS usability and achievement motivation. Study I (n = 2200) used exploratory factor analysis to establish a 19-item unifactorial structure explaining 76.55% of the variance. Study II (n = 3606) confirmed the factor structure via confirmatory factor analysis, established full measurement invariance across gender, and demonstrated high discriminatory power via IRT and strong criterion-related validity. Study III (n = 1076) tested mediation models, revealing that internal and external locus of control partially mediated the relationship between perceived LMS usability and achievement motivation. Specifically, higher perceived usability was positively associated with internal locus of control and negatively associated with external locus of control. These findings suggest that well-designed digital learning environments can foster autonomous motivational orientations conducive to sustained academic engagement. Full article

(1) Background: Student burnout, widely regarded as a form of “hidden dropout” among adolescents, is associated with lower educational quality and mental health. Grounded in the Study Demands–Resources (SD–R) and Conservation of Resources (COR) theories, this study investigates the relationship between school-based resources, family dynamics, and personal resources by examining how teacher emotional support is associated with burnout through family cohesion and meaning in life; (2) Methods: a moderated mediation model was tested using a sample of 1224 adolescents (Mage = 14.27, SD = 1.72; 48% female); (3) Results: Analysis revealed that: 1. Teacher emotional support significantly and negatively predicted student burnout (β = −0.28, p < 0.001). 2. Family cohesion partially mediated this relationship, accounting for 36% of the total effect. 3. Meaning in life significantly moderated both the direct path and the second half of the mediation pathway (family cohesion → burnout). Notably, meaning in life was associated with a stronger negative association between teacher emotional support and student burnout, but a weaker negative association between family cohesion and student burnout, a pattern consistent with differential resource utilization; (4) Conclusions: These findings suggest a differentiated pattern of resource interplay: school-based emotional resources may connect to family-based relational resources, and the protective role of each external resource may be further moderated by adolescents’ internal meaning systems. These findings highlight the agentic role of adolescents in resource management and point to the value of multi-system interventions. Full article

The study examines discrepancies between personally reported and declared use of generative artificial intelligence (GenAI) among university students from a public university located in the Peruvian Andes, operationalized as the AI Use Gap, an exploratory discrepancy indicator based on two self-reported measures. Drawing on a sequential explanatory mixed-methods design, the study combines survey data (N = 150), experimental vignette evaluations, and qualitative follow-up interviews to explore how students manage the visibility and disclosure of AI use in academic contexts. Findings indicate relatively high levels of AI use alongside a consistent discrepancy between personally reported and declared use, suggesting patterns of differential reporting across contexts. Quantitative analyses did not show clearly differentiated exploratory relational patterns between the AI Use Gap and the psychosocial/contextual indicators examined, including perceived stigma, concealment, normative ambiguity, and peer pressure. Given the exploratory nature and limited internal consistency of the contextual indicators, these findings were interpreted cautiously as provisional exploratory patterns rather than as evidence of stable psychosocial relationships. Qualitative findings suggest that AI disclosure practices are shaped by socially evaluative and context-dependent processes, including fear of judgment, uncertainty regarding acceptable AI use, and selective disclosure strategies. Participants frequently described AI use as widespread but not consistently disclosed across academic settings. Overall, the findings suggest that discrepancies between AI use and disclosure may be better understood as forms of visibility management shaped by institutional ambiguity and social expectations rather than by stable individual-level characteristics alone. Rather than validating stable psychosocial mechanisms, the study explores an emerging and context-sensitive phenomenon using provisional contextual indicators intended to capture heterogeneous patterns of perception and disclosure. The study contributes to ongoing discussions regarding transparency, academic integrity, and the social regulation of AI use in higher education, particularly in under-researched Global South contexts. Full article

Financial literacy among university students is increasingly important in contexts marked by digital payments, accessible credit and growing financial-product complexity. This study analyzes the explanatory relationships between technical-financial knowledge (TFK), perception/attitude toward financial education (PS), practical application of financial knowledge (PAK), and financial self-management (PFS) among Peruvian university students. A total of 422 surveys were collected, and the final PLS-SEM analysis was conducted with 358 complete cases. The model was estimated in ADANCO using consistent PLS for reflective constructs and Mode B for PFS as a formative construct, with 5000 bootstrap replicates. The results show that TFK positively predicts PS (β = 0.711; p < 0.001) and PAK (β = 0.709; p < 0.001). PFS is explained by both PS (β = 0.282; p < 0.001) and, more strongly, PAK (β = 0.558; p < 0.001), with moderate-to-high explanatory power (R² = 0.568). The total indirect effect of TFK on PFS was significant (β = 0.596; p < 0.001), and the TFK → PAK → PFS pathway was the dominant mechanism. These findings suggest that university financial education should move beyond conceptual content and prioritize practice-oriented learning strategies, including budgeting, savings planning, product comparison and digitally mediated decision-making. Full article

Graduate students face increasing academic and psychological pressures, making it important to identify educational practices associated with their mental health. This study examined the association between labor education and graduate students’ mental health by constructing a moderated mediation model. A cross-sectional survey was conducted among 1283 full-time graduate students in Chinese universities. Structural equation modeling and PROCESS analyses were used to test the hypothesized relationships. The results showed that labor education was positively associated with graduate students’ mental health. Bootstrap analyses further supported three sequential mediation pathways: self-efficacy and psychological resilience, meaning in life and time management ability, and problem-solving ability and emotion regulation ability. These indirect pathways collectively account for approximately 40% of the total effect. In addition, supervisor support was found to strengthen the positive association between labor education and self-efficacy, while peer support strengthened the positive association between labor education and problem-solving ability. These findings suggest that labor education may function as a resource-building educational practice in graduate training and that academic social support may condition its association with students’ psychological and behavioral resources. This study contributes to research on graduate student well-being by linking labor education with psychological resources, cognitive appraisal, behavioral adaptation, and academic social support. Full article

Nursing students in their training process often suffer from anxiety due to stressful situations, and emotional intelligence can help them to manage these situations. The aim of this study is to analyse the associations between the dimensions of perceived emotional intelligence and anxiety in students undergoing their training cycles in different special services in order to check if there are differences between them. It is an observational, cross-sectional and correlational study with a sample of 85 nursing students who had not received training in emotional intelligence. Two measurement instruments were used: the Trait-State Anxiety Inventory (STAI) to assess anxiety and the Trait Meta-Mood Scale (TMMS-24) to measure EI. Data were analysed using Pearson’s coefficient when the distribution was normal, and Spearman’s coefficient in the non-normal distribution. The results showed in the group—ER-Emergency and Oncology—there was a significant negative relationship between state and trait anxiety and emotional understanding and regulation. In contrast, in the Primary Care setting there was also a positive relationship between emotional perception and trait anxiety. The study concludes that nursing students who understand and manage their emotions may have a lower risk of anxiety. Furthermore, if they identify emotions appropriately, the risk of suffering from anxiety in the long term may be lower. This finding was observed when the student did the internship in Primary Care. So there is a difference depending on the clinical context. Full article

Background: University students experience increased psychological distress during academic transitions, yet modifiable lifestyle determinants of their subjective well-being remain incompletely understood within integrated analytical frameworks. Methods: A cross-sectional survey (N = 194 undergraduates; 52.6% women; M age = 21.9 years) used validated instruments: the Satisfaction with Life Scale (SWLS), Perceived Stress Scale (PSS-10), Body Appreciation Scale-2 Short Form (BAS-2SF), Brief Resilience Scale (BRS), International Physical Activity Questionnaire Short Form (IPAQ-SF), and single-item measures of financial security and screen time. Physical activity (PA) was log-transformed (MET_log). Multiple simultaneous regression and structural equation modelling (SEM) were conducted. Results: Perceived stress was the strongest negative predictor of life satisfaction (B = −0.561, p < 0.001), while financial security was a significant positive predictor (B = +0.171, p = 0.023). SEM showed that body appreciation was associated with life satisfaction primarily through lower perceived stress (indirect effect = 0.107; consistent with indirect-only association pattern), while PA showed a significant direct association with life satisfaction (β = +0.143, p = 0.030), independent of the stress pathway. The indirect effect of PA via stress was not significant in the SEM. Model fit was acceptable (CFI = 0.951; RMSEA = 0.067). Conclusions: Perceived stress was statistically associated with the relationship between body appreciation and subjective well-being, while PA showed a direct statistical association with well-being that was independent of the stress pathway. Given the cross-sectional nature of this study, these findings suggest that university health promotion programmes may consider integrating positive body image and stress management components alongside PA promotion to support student psychological well-being. Full article

Automated data exploration is very useful for evaluating key aspects of populations such as young adults (which here refers to the youth population in the United States represented by students in grades 9 through 12). This article shows how Principal Component Analysis (PCA) can be used for this exploration. PCA is applicable to data analysis situations with data from $n$ individuals of $m$ attributes (generally $n >> m$). For analytical purposes, the data can be visualized as $n$ points in a Euclidean space with Cartesian coordinates, with $m$ perpendicular coordinate axes, where each axis corresponds to an attribute. When $m$ is large, the points become difficult to visualize, so PCA is useful, as it is a dimensionality reduction method that facilitates the visualization of the points. The objective of this article is to identify relationships between attributes, where there is a primary attribute of interest. The present work describes some of the main theoretical aspects of PCA and then uses PCA to analyze data, as a practical example. The data comes from the publicly available results of a 2023 survey administered to a nationally representative sample of students in the United States, to assess health risk behaviors among young adults (students in grades 9 through 12), which was conducted by the Youth Risk Behavior Surveillance System (YRBSS), managed by the Centers for Disease Control and Prevention—CDC. The results of this work graphically discover relationships between specific data attributes. The reliability of the results is then discussed, considering: (1) recommendations taken from PCA literature, and (2) the use of a graphical tool called a Zoning Biplot, an improved form of displaying PCA results. This work is relevant because it uses the Zoning Biplot, proposed by the authors, which shows more detail in the results compared to a conventional Biplot; the authors argue that this detail allows for valid results across a larger number of datasets, such as the dataset in the example presented. The authors present a graphical development to support the concept and advantage of a Zoning Biplot. Full article

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality. Full article

The steady advancement of online education has not automatically translated into improved educational quality. Teacher training often continues to focus on the technical use of digital tools, while the pedagogical processes through which teachers report supporting students’ digital competence remain insufficiently understood. The objective of this study was to examine the sequential and predictive structure of teachers’ digital competence using the DigCompEdu framework as a reference. A quantitative cross-sectional study was conducted with a sample of 136 university teachers involved in online education. Data were collected through a self-reported questionnaire based on DigCompEdu and analyzed in two phases: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANNs). The PLS-SEM results suggested a sequential pattern of associations among teacher-reported constructs: Professional Commitment (PC) was positively associated with Digital Resource Management (DR), which in turn was positively associated with Digital Pedagogy (DP) and Assessment and Feedback (AF). These dimensions were associated with Student Empowerment (SE), which showed the strongest positive relationship with teachers’ reported practices for Facilitating Students’ Digital Competence (FS). The ANN sensitivity analysis showed adequate predictive performance in the testing phase (RMSE = 0.155) and identified Student Empowerment as the predictor with the highest normalized importance within the specified model. These findings suggest that faculty development in online higher education may benefit from moving beyond basic digital literacy and platform management toward pedagogical design, formative assessment, inclusive participation, and learner agency. However, the results should be interpreted as evidence of teacher-reported facilitation practices within the analyzed sample, rather than as direct evidence of students’ actual digital competence development. Full article

This study examines the perceived usefulness, perceived benefits, and acceptance of artificial intelligence (AI) technologies in tourism, with a specific focus on Generation Z. Drawing on established technology acceptance frameworks, the research investigates how key perceptual factors influence the adoption of AI in travel planning and tourism services. The empirical analysis is based on a questionnaire survey conducted among 531 university students from Slovakia. The study employs factor analysis, correlation analysis, regression modeling, and non-parametric tests to explore relationships between perceived usefulness, perceived benefits, acceptance, trust, and experience with AI technologies. The results reveal strong and statistically significant relationships among all three core constructs. However, regression analysis indicates that perceived usefulness does not directly influence acceptance when perceived benefits are included, suggesting a mediating effect. Perceived benefits emerge as the strongest predictor of acceptance, emphasizing the importance of experiential value, such as efficiency, personalization, and improved decision-making. Trust in AI-generated travel information and perceptions of AI’s contribution to quality of life significantly influence all constructs. Additionally, prior experience with AI tools positively affects user attitudes. The findings suggest that AI adoption can enhance tourism competitiveness and support tourism development, provided that trust, information quality, and human–technology balance are effectively managed. Full article

Understanding the motivational factors influencing student teachers’ self-efficacy in adopting Artificial Intelligence (AI) is essential in technology-driven learning environments, but this area has received less research attention in resource-scarce settings like Ethiopia. To this end, this study was initiated to explore the motivational factors influencing the self-efficacy in adopting AI among Ethiopian student teachers. The study employed structural equation modeling to analyze data collected from 278 student teachers enrolled in teacher education programs to determine the relationship between motivational factors (commitment to the teaching profession, along with intrinsic, extrinsic, and altruistic motivations) and dimensions of self-efficacy (teaching AI skills, planning and classroom management, and student affective domains). The result demonstrated that strong and positive associations were found between affective commitment to teaching and self-efficacy (p < 0.01) in AI teaching skills, planning and managing the classroom, and addressing the student affective domain. In addition, positive and moderate associations were noted between extrinsic motivation and self-efficacy (p < 0.05) in the student affective and teaching AI skills domains. No significant relationships were observed for intrinsic or altruistic motivations. Thus, by highlighting the role of commitment and extrinsic motivation, the findings can inform teacher education programs aiming to enhance the holistic development and effectiveness of future educators and contribute to developing targeted recruitment and training strategies that nurture motivated and technologically proficient teachers. Full article