Search Results (1,001)

College students’ physical activity may decline when academic demands are high, even among those who previously intended to exercise. This three-wave longitudinal study examined whether T2 physical activity, modeled conditional on T1 exercise intention and T1 physical activity, was involved in a time-ordered indirect association between T1 academic stress and T3 depressive and anxiety symptoms. Of 982 students who completed the baseline survey in September 2025, 750 provided complete matched data in December 2025 and March 2026 (retention = 76.4%). Primary models used raw T2 physical activity and adjusted all component, direct, and total-effect equations for the identical covariate set: gender, age, BMI, T1 exercise intention, T1 physical activity, T1 PHQ-9, and T1 GAD-7. A sign-reversed residualized activity score was examined only as an algebraically equivalent parameterization. T1 exercise intention predicted higher T2 physical activity (β = 0.308), whereas T1 academic stress was associated with lower T2 physical activity (B = −12.771, p < 0.001). Under identical adjustment, raw T2 physical activity was inversely associated with T3 depressive symptoms (B = −0.0024) and anxiety symptoms (B = −0.0013); the corresponding sign-reversed residualized coefficients were equal in magnitude and opposite in sign, with identical R² values (0.4257 and 0.2720). Full-process bootstrap indirect associations were significant for depressive symptoms (B = 0.0309, 95% CI [0.0229, 0.0396]) and anxiety symptoms (B = 0.0162, 95% CI [0.0099, 0.0236]). The Stress × Intention interaction and quadratic residualized-activity terms were not significant. No observed T1 variable differed significantly between retained and attrited participants. Higher academic stress was associated with relatively lower subsequent physical activity, which was in turn statistically associated with later depressive and anxiety symptoms. The findings do not establish causality, disruption of intention enactment, or a distinct psychological residual mechanism. Full article

Most predictive approaches in educational data mining rely on complex models whose opacity limits practical adoption by classroom teachers, creating a gap between model sophistication and classroom usability. This gap is particularly acute at the class-group level, where institutional gradebook data are routinely aggregated for teacher-level planning but rarely modelled with an explicit account of when model complexity is actually justified. This paper addresses that gap: its novelty is to provide a structural explanation, grounded in group-level academic dynamics, for why linear models are highly competitive, rather than merely adequate, for this type of data, and to test this account empirically. An eight-year longitudinal dataset (2013/2014–2020/2021) from a Spanish secondary school—1070 class-group records across 32 subjects—was used to compare linear regression and Random Forest for final grade prediction, a Random Forest classifier against an XGBoost classifier for academic risk detection, and SHAP (SHapley Additive exPlanations)-based explainability, validated through Leave-One-Course-Out (LOCO) cross-validation. Within this dataset, linear regression consistently matches or outperforms Random Forest in both scenarios (R² = 0.857 with two assessments; R² = 0.740 with one), explained by stable cohort dynamics—baseline grades, teaching continuity, group composition—that produce a linear temporal structure (Spearman ρ > 0.81) leaving little predictive return for ensemble complexity in this setting. For the passing class, the Random Forest classifier achieves F1 = 0.972 with high inter-cohort stability (LOCO F1 ∈ [0.944, 0.984]); for the minority at-risk class, it outperforms XGBoost (F1 = 0.69 vs. 0.57), a gap consistent with the benefit of explicit class-imbalance handling, though fully disentangling this from a possible ensemble-family effect is left for future work. The 2019/2020 cohort is statistically anomalous (Mann–Whitney U, p < 0.001), reflecting an exogenous shift in the grade-generating process under emergency evaluation rather than evidence against the linearity account under normal conditions. Simple, transparent models operating on routinely collected gradebook data deliver actionable early-warning signals within the digital competence of most practising teachers; group-level prediction additionally protects student identity by ensuring no individual is labelled at-risk, combining predictive utility with ethical design. Full article

Open workforce datasets can help to extend research on artificial intelligence (AI) only if they are sufficiently provenance-traceable, have good measurement quality, and have a relational structure suitable for behavioral inference. This study examines a benchmark dataset comprising 12,000 linked records across 15 industry categories and 47,206 AI tool-use records. Sampling, recruitment, questionnaire wording, respondent authentication, ethics procedures, and whether the records are real or synthetic are not documented, so the dataset is treated as a tabular source rather than verified workforce evidence. Analyses are limited to indicators that have been observed directly, including job satisfaction, work–life balance, career outlook, trust in AI, weekly learning hours, AI-use intensity, and employer-provided AI training. Assessment of behavioral interpretation of the data was conducted using Spearman correlations, HC3-robust regressions, false discovery rate adjustment, secondary industry interaction checks, and a relational-realism diagnostic. Concerns about AI replacing jobs were negligible and non-significant on a five-point scale, with an average of 2.745. Training also showed no robust association when evaluated against outcomes that did not contain training. The median absolute Spearman correlation across conceptually related observed variables was 0.0064, with the 95th percentile at 0.0180, which is very low. These estimates reflect the characteristics of the supplied documents rather than employee actions, and do not assess conservation of resources processes or tourism worker outcomes. Results from the analysis demonstrate the importance of checking open workforce data for construct validity, relational validity, sector fit and provenance before testing behavioral theories. Research for regenerative tourism requires reliable sector-specific samples, reliable multi-item measures, longitudinal design, and direct measures of social and destination outcomes. Full article

Background/Objectives: Lateral flow assays (LFAs) are widely used point-of-care diagnostic devices due to their low cost, portability, and ease of use. However, most LFAs provide only qualitative results, limiting their utility for applications requiring continuous biomarker monitoring. This study introduces DeepBand, a deep learning-enabled multi-stage framework designed to automate the continuous quantification of analyte concentrations from unstandardized smartphone-captured lateral flow assay (LFA) images. Methods: A publicly available dataset containing 672 COVID-19 LFA images corresponding to four analyte concentrations (0.0, 1.8, 3.7, and 7.4 ng) was analyzed. A multi-stage pipeline was developed consisting of: (1) YOLOv11-based object detection to isolate the LFA cartridge from background artifacts, (2) a custom computer vision algorithm to identify and crop the test and control bands, and (3) a custom convolutional neural network (CNN) trained as a supervised regression model to predict continuous analyte concentrations. Data augmentation, hyperparameter optimization, and 5-fold cross-validation were used to improve model robustness. Results: The YOLOv11 model achieved approximately 99% mAP50 and 93.96% mAP95 for cartridge detection. Initial CNN models exhibited systematic underprediction of higher concentrations due to target imbalance; replacing mean squared error with Huber loss substantially improved performance, resulting in a final 20% held-out test set RMSE of 0.0292 ng. Analysis of HSV image channels demonstrated that the saturation-channel test-to-control intensity ratio was strongly correlated with analyte concentration (r = 0.94), consistent with the Beer–Lambert law governing LFA signal formation. Channel ablation studies confirmed the saturation channel as the most informative feature, while saliency mapping showed that the model primarily focused on biologically relevant test and control line regions. Conclusions: The proposed deep learning-enabled workflow, DeepBand, successfully integrates object detection, image processing, and CNN-based regression to provide automated quantitative interpretation of LFA results from smartphone images. Furthermore, the observed agreement between model behavior and Beer–Lambert theory suggests that the network learns biologically meaningful signal characteristics, supporting its potential for quantitative point-of-care diagnostics and longitudinal disease monitoring. Full article

Financial crises are often expected to trigger industry renewal through processes of creative destruction; however, this outcome is not automatic. This study examines why the mechanisms associated with creative destruction failed to emerge in Lebanon’s banking sector during the crisis. Using a qualitative longitudinal case study and process-tracing approach, the analysis draws on regulatory documents, financial data, institutional reports, and secondary sources covering the period from 2014 to 2024. The findings show that successive regulatory interventions contained short-term pressures while preventing the restructuring mechanisms associated with industry renewal from emerging. Instead, these responses preserved a weakened banking structure and reinforced three interconnected mechanisms: strategic inertia, capability erosion, and the failure of the restructuring mechanisms associated with creative destruction. Over time, banks lost much of their capacity to perform core financial functions, public trust declined, and households and firms increasingly relied on cash and informal financial channels. The consequences extended beyond the banking sector, weakening financial intermediation, investment, monetary stability, and broader economic recovery. The study contributes to strategic management theory by showing that creative destruction is not an automatic response to crisis but an institutionally mediated process. The Lebanese case demonstrates how weak governance, the absence of credible restructuring, and limited mechanisms for institutional exit and resource reallocation can prevent industry renewal from emerging. It further shows how prolonged reliance on short-term crisis management can erode organizational capabilities and preserve dysfunctional institutions rather than promote adaptation and renewal. Full article

This study analyses how Spanish digital news outlets covered the arrival of artificial intelligence in journalism and the social impact they attributed to it. That general objective is pursued through four specific research objectives rather than through hypotheses in a descriptive and exploratory design that traces the emergence and distribution of media attention, identifies dominant and omitted issue-specific frames, and assesses their association with journalistic variables. A longitudinal quantitative content analysis covers 242 news items from ten leading Spanish digital outlets, coded in their entirety rather than sampled, across the three phases of AI’s public visibility between 2022 and 2024. Coverage follows a compressed attention cycle in which enthusiasm and alarm unfold simultaneously rather than consecutively. Dominant frames centre on generative AI in the processing phase, credibility, ethics in the use of data, legal responsibility and information quality, and timeliness and accuracy as its main advantage and disadvantage. Systematically omitted are the effects of AI on the normative functions of journalism. The main source is the variable most closely associated with these patterns: expert sources broaden the range of frames, whereas the overrepresented media-sector actors narrow it. Spanish digital media thus construct AI as an operational rather than a democratic issue. Full article

Travel agencies and tour operators employ large numbers of women, yet the evidence remains fragmented on whether employment provides comparable rewards, working conditions, career opportunities, and organizational authority. This scoping review mapped indexed empirical research on gendered employment and organizational power in tourism intermediaries. Scopus and the Web of Science Core Collection were searched through 20 July 2026. After deduplication, 390 unique records were independently screened by two reviewers, 30 reports underwent full-text assessment, and 17 reports representing 16 studies were included. Data were extracted using a structured matrix and synthesized through evidence-role classification, design-sensitive interpretation, methodological robustness appraisal, and sensitivity analysis. The evidence base was small and heterogeneous. Thirteen studies provided direct and separable evidence, whereas three were contextual. The clearest findings concerned unequal wage returns, work-design conditions associated with retention and safety, and selective routes into leadership or ownership. Evidence from three settings did not support a simple capability-deficit explanation, while strategic decision rights, collective voice, board processes, and platform governance were rarely examined directly. The review organizes these findings around reward allocation, work organization, and access allocation. Future research should broaden database coverage and use longitudinal, comparative, and decision-level designs to examine these allocation processes directly. Full article

Background/Objectives: Irritable bowel syndrome (IBS) is one of the most common disorders of gut–brain interaction and one of the most frequent functional bowel disorders encountered in clinical practice. The lack of reliable biomarkers, coupled with inconsistent clinical presentations and variable therapeutic outcomes, creates a landscape of ongoing uncertainty for clinicians. Against this background, non-pharmacological and technological developments—such as digital health interfaces, sensor technologies, and medical devices—emerge as potential support tools for clinical management. This qualitative study explored how healthcare professionals manage diagnostic and therapeutic uncertainty in IBS across care settings while evaluating the potential role and value of integrating these technological and non-pharmacological innovations into routine practice. Methods: Fifteen semi-structured interviews were conducted with healthcare professionals involved in IBS care across primary and specialized settings and one expert patient with lived experience in the Barcelona metropolitan area. Data were analyzed using principles of grounded theory. Results: The notion of “catch-all category” articulates four interrelated themes that characterize IBS in clinical practice: persistent clinical uncertainty, pragmatic diagnostic strategies, patient engagement in biopsychosocial and longitudinal management, and the implementation and interpretability of digital and non-pharmacological support. Participants described IBS management as shifting from exclusionary, symptom-based diagnostics toward holistic biopsychosocial and gut–brain-axis models. This transition underscores the necessity for resources that empower patients through education, support clinicians through longitudinal monitoring, and facilitate shared management strategies. Conclusions: The findings suggest that IBS management is not a single diagnostic act but a longitudinal process of uncertainty management. Future digital or non-pharmacological support tools for IBS should prioritize longitudinal monitoring, clinically interpretable information, and low burden for professionals and patient–professional communication. Full article

Preparing graduates to navigate increasingly uncertain labor markets is an important challenge for higher education and the broader pursuit of sustainable graduate employability. Although higher education institutions invest substantially in developing Career Competencies (CC), comparatively less is known about how these competencies are associated with graduates’ Job Market Readiness (JMR), the role of Career Self-Management (CSM) in this relationship, and whether perceived Labor Market Uncertainty (LMU) conditions the strength of this association. Drawing on Human Capital Theory, Career Construction Theory, and Conservation of Resources Theory, this study develops and empirically examines an integrated framework linking developmental resources, self-regulatory career behavior, employment readiness, and perceived labor market conditions. Data were collected from 398 final-year university students and recent graduates in Saudi Arabia using a non-probability, multi-institutional purposive sampling approach and analyzed using IBM SPSS Statistics 27, AMOS 27, and Hayes’ PROCESS macro. The findings show that CC was positively associated with both CSM and JMR, while CSM was also positively associated with JMR. The analysis further identified a significant indirect statistical association between CC and JMR through CSM, along with a remaining significant direct association. In addition, perceived LMU significantly moderated the CC–JMR relationship, such that the positive association was stronger at higher levels of perceived uncertainty. These findings contribute to graduate employability research by showing that the employment relevance of Career Competencies is associated not only with the resources graduates possess but also with their Career Self-Management and the labor market conditions they perceive. Therefore, sustainable graduate employability is treated as a broader theoretical implication of these relationships rather than an outcome directly measured by this study. These findings provide a basis for future longitudinal research examining whether these associations contribute to more sustainable education-to-work transitions over time. Full article

Loneliness among older men may remain unrecognized when unwanted relational disconnection is not explicitly named as loneliness. This qualitative longitudinal, multimethod study examined how 32 community-dwelling men aged 60 years and older in Lisbon, Portugal, experienced, interpreted, and communicated relational disconnection between January and July 2025. Data were generated through narrative life-history interviews, participant-generated photographic, audio, or written diaries, elicitation interviews, and follow-up interviews. Six interrelated themes were developed and interpreted processually as disruptions and absences, forms of disconnection, identity-protective meaning-making, and responses. The analysis distinguished topics deliberately elicited by the design from empirical refinements and unanticipated temporal or material insights. The four processes initially used to sensitize inquiry—nonrecognition, linguistic substitution, identity protection, and relational concealment—were therefore not treated as discoveries; the data clarified their boundaries, interactions, and reversibility. Four longitudinal patterns were used as non-exclusive interpretive summaries rather than participant classifications. The most distinctive findings were that connection depended not only on contact but on relational consequentiality—being expected, needed, consulted, and able to contribute—and that greater willingness to name loneliness could reflect interpretive change rather than worsening symptoms. Unnamed loneliness was inferred only where participants described unwanted relational disconnection; chosen and satisfying solitude was excluded. Responses should foster emotionally safe, reciprocal, and meaningful relationships while protecting autonomy. Full article

Predicting academic performance in face-to-face computing and software engineering courses is hindered by the limited availability of fine-grained process data. This study proposes STEAP, a camera-based static–temporal fusion framework that integrates longitudinal classroom behavior sensing with conventional educational records. Classroom videos from 375 undergraduates enrolled in four computing-related courses were collected over nine teaching weeks. Camera-derived observable behaviors were organized into student-level weekly sequences and transformed into outcome-independent longitudinal representations. Multiple machine-learning classifiers were subsequently applied to predict students’ academic performance. Checkpoint-specific predictions were conducted at Weeks 3, 6, and 9, with each prediction using only the classroom behavioral information available up to the corresponding time point. Using the complete nine-week Temporal representation together with the pre-course Background variables, XGBoost achieved the strongest classification performance among the evaluated models, with an Accuracy of 0.867, a Macro F1 of 0.862, and an At-risk Recall of 0.924. The checkpoint analyses further indicated that classroom behavioral information collected during the early course stage already provided useful predictive information without incorporating behavioral observations from subsequent weeks. After further integrating pre-course background variables and regular assessment information, the final fusion model achieved an Accuracy of 0.896 and a Macro F1 of 0.895. Overall, longitudinal camera-derived classroom behavior provides complementary predictive information beyond conventional educational information and supports the feasibility of earlier academic-risk identification at different course checkpoints. Full article

Quality assurance in Initial Teacher Education (ITE) has evolved from compliance-oriented approaches toward more comprehensive frameworks emphasizing continuous improvement, evidence-informed decision-making, and educational sustainability. Nevertheless, across Latin America, persistent fragmentation between accreditation processes, institutional data systems, and teacher education practices continues to limit universities’ capacity to address complex organizational and territorial challenges. This study aimed to develop an empirically informed conceptual framework for evidence-based quality assurance in ITE. A convergent mixed-methods design was employed, integrating a quantitative longitudinal component based on publicly available official secondary data covering 1743 students enrolled in teacher education programs at a regional Chilean university between 2020 and 2025 with a qualitative longitudinal documentary analysis of accreditation, institutional, regulatory, and territorial evidence covering the 2012–2025 period. Both components were analyzed independently and subsequently integrated through convergent triangulation during the interpretative phase. The quantitative findings revealed a heterogeneous student population characterized by predominantly female enrollment, differentiated academic trajectories, territorial concentration with additional geographic diversity, and descriptive variation in retention across teacher education programs. Documentary evidence identified recurrent strengths in institutional purpose, curricular development, and territorial engagement, alongside persistent challenges related to information integration, organizational workload, evidence-informed curricular improvement, and academic capacity. The convergence and complementarity of these findings informed the development of the Evidence-Based Excellence Management Model (EBEMM), which integrates student entry characteristics, institutional processes, observed academic outcomes, quality-assurance evidence, and territorial context within a continuous-improvement framework. The EBEMM is presented as an empirically informed conceptual framework rather than a formally validated causal, predictive, or operational model. Future multi-institutional research should examine its structural relationships, evaluate its potential predictive applications where appropriate, and assess its external applicability. Full article

Depressive symptoms, alcohol use, and body mass index (BMI) frequently co-occur, yet their longitudinal associations from adolescence into adulthood remain unclear. This study examined their prospective associations by distinguishing between-person differences from within-person changes and testing variations by gender and race. Data came from four waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health). The analytic sample comprised 5734 participants (51.4% female; 56.7% non-Hispanic white) followed from ages 11–19 to ages 24–32. Multigroup cross-lagged panel models (CLPMs) and random intercept cross-lagged panel models (RI-CLPMs) assessed associations across four gender–racial groups: white males, white females, non-white males, and non-white females. All three variables demonstrated significant stability over time. Higher adolescent depressive-symptom levels were associated with higher subsequent alcohol-use scores at both the between-person and within-person levels. Higher BMI in young adulthood was associated with higher subsequent depressive-symptom levels at the between-person level. At the within-person level, higher-than-usual depressive-symptom levels were associated with lower subsequent BMI only among white females during adolescence. Within-person associations between alcohol use and BMI also varied developmentally: higher-than-usual alcohol-use scores were associated with higher subsequent BMI earlier in development, whereas negative associations emerged in both directions in adulthood. Several associations differed across gender–racial groups. Overall, the findings demonstrate complex, developmentally specific associations among depressive symptoms, alcohol-use frequency, and BMI, underscoring the importance of distinguishing between-person from within-person processes and considering gender, race, and developmental stage. Full article

Background/Objectives: Executive function (EF) is a foundational cognitive process associated with academic achievement and socioemotional adjustment. While average developmental trends are well documented, less is known about heterogeneity in EF trajectories during early childhood and the contextual factors associated with different developmental pathways. This study examined longitudinal trajectories of EF between ages 3 and 6 and their associations with family, child, and caregiver well-being, as well as the home environment. Methods: Data were drawn from the Chilean longitudinal cohort First Thousand Days (Mil Primeros Días [MPD]; n = 583). EF was assessed at ages 3, 5, and 6 using the Cat-Dog task. Latent profile analysis identified EF trajectories, and multinomial logistic regression examined predictors of trajectory membership. Results: Three EF trajectories were identified: Normative (54.5%), Persistently Disadvantaged (34.6%), and Advantaged Accelerating (10.8%). Home environment quality was the most consistent predictor of trajectory membership, increasing the likelihood of following the Advantaged Accelerating (RRR = 1.59) or Normative (RRR = 1.21) trajectory relative to the Persistently Disadvantaged trajectory. Early cognitive development protected against the most disadvantaged trajectory, whereas distal sociodemographic characteristics and caregiver well-being were not significant predictors of trajectory membership. Conclusions: Individual differences in EF emerge early and show divergent growth rates through age 6, with home environment quality as the strongest predictor—highlighting the value of enriching home learning environments before school entry. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming the landscape of rheumatological and systemic inflammatory disease management, offering unprecedented capacity to integrate complex, multidimensional data for diagnostic support, disease monitoring, and therapeutic decision-making. This comprehensive narrative review, based on a non-systematic literature search of PubMed/MEDLINE and Google Scholar combined with the authors’ clinical expertise, provides a clinically oriented synthesis of current and emerging AI applications across the full spectrum of immune-mediated inflammatory diseases—including rheumatoid arthritis, systemic lupus erythematosus, vasculitis, inflammatory bowel disease, psoriatic arthritis, systemic sclerosis, inflammatory myopathies, and sarcoidosis—with particular attention to applications that have demonstrated or are approaching clinical utility. We discuss deep learning-based image analysis, natural language processing of electronic health records, multi-omic biomarker discovery, and the application of Fourier transform-based signal processing to biological time series as a novel approach to continuous disease monitoring. Fourier transform methods—already foundational in MRI reconstruction, cardiac electrophysiology, and clinical neurophysiology—are here systematically extended to rheumatological and inflammatory disease signals, including accelerometry, electromyography, heart rate variability, and longitudinal biomarker time series. The phenomenon of large language model hallucination—particularly critical in rare inflammatory diseases—is addressed alongside retrieval-augmented generation as a mitigation strategy. We further argue that AI-driven methods do not merely improve the interpretation of clinical data, but fundamentally expand what is observable—with profound epistemological implications for clinical knowledge transmitted through generations of medical tradition. Ethical considerations and future directions toward precision inflammatory disease medicine are outlined. Full article