Search Results (342)

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

Diet quality, nutrition knowledge, and psychosomatic literacy—defined as the understanding of the interactions between diet, gut microbiota, and mental well-being—may shape weight-related behaviours in youth. This study used a cross-sectional design to integrate these domains with digital information pathways in Central–Eastern Europe. This study assessed diet quality, nutrition, and psychosomatic knowledge, supplement use, and health-information sources among Polish adolescents and young adults, with emphasis on age-related differences and the role of social media. A cross-sectional, anonymous online survey (October 2025–January 2026) was conducted in Poland (final analytical sample: n = 478; adolescents 15–19 years vs. young adults 20–30 years). Of 591 individuals who accessed the survey, 478 were included in the final analytical sample. Diet quality was estimated from FFQ data using KomPAN-derived indices (pHDI-10, nHDI-14, DQI). Nutrition knowledge (0–25 points), psychosomatic/gut–brain indicators, supplementation, and information sources were analysed using χ²/Fisher tests and Mann–Whitney U tests with effect sizes. The primary outcomes measured were dietary supplement use and excess body weight (BMI ≥ 25 kg/m²). Multivariable logistic regression examined predictors of supplement use and BMI ≥ 25 kg/m². Overall diet quality was low to moderate, with limited intake of whole grains, legumes, and fish, and common nutrition misconceptions. Social media was the most frequently indicated source of diet/supplement information and was independently associated with more frequent supplement use (OR = 2.29; 95% CI: 1.43–3.64). Adolescents reported lower whole-grain intake and more misconceptions than young adults. Predictors of BMI ≥ 25 kg/m² included male sex (OR = 2.46; 95% CI: 1.46–4.15), lower education, and lower nutrition knowledge, while age showed a non-linear positive association with excess body weight. Polish adolescents and young adults show gaps between declared pro-health attitudes and actual diet quality/competencies. Social media reliance appears particularly linked to product-oriented behaviours (supplementation). Prevention should strengthen nutrition and food safety education, digital health literacy, and professional guidance on supplementation, especially in adolescents. Our findings suggest that social media is a primary driver for dietary supplementation among Polish youth, more so than objective nutrition knowledge. While diet quality is linked to weight status, the relationship is complex. These results may inform future public health interventions targeting digital health literacy to promote balanced nutrition and safe supplementation practices. Full article

Hungarian teachers’ reform priorities remain insufficiently mapped, despite their central role in shaping feasible, evidence-based educational change. In a cross-sectional study with 1254 kindergarten, primary, and secondary teachers across Hungary (May 2025), we elicited and analyzed open-ended written responses in which participants identified their top three required reforms. Responses were segmented and coded into 18 mutually exclusive categories via a validated codebook, and prevalence was calculated using respondent-normalized weights. We then examined demographic, well-being, and personality correlates of reform priorities using χ² tests, Mann–Whitney tests, and multivariable logistic models with Benjamini–Hochberg false discovery correction. Teachers most frequently prioritized competency development and pedagogical reform, followed by curriculum flexibility and system governance. Reform priorities were not random: female teachers were substantially more likely to prioritize inclusion and SEN support, while male teachers more often prioritized governance and depoliticization; older age predicted governance priorities. Lower educational system satisfaction robustly predicted prioritizing curriculum reform, autonomy, and governance restructuring, and anxiety and depression were positively related to curriculum concerns. Conscientiousness predicted prioritizing salary and material recognition. The results indicate that teachers’ reform demands function as systematic, psychologically grounded signals that can guide more targeted, teacher-centerd educational policy in Hungary. Full article

Background/Objectives: Patient-Reported Experience Measures (PREMs) help us understand how patients perceive healthcare quality. Yet most studies look at complaints in isolation, without tying them to the structural features of medical practice. This study asks whether the nature of clinical work—shaped by diagnostic pathways, interaction patterns, and professional focus—predicts what patients complain about. Methods: We analyzed 18,492 negative reviews from infodoctor.ru, collected between 2012 and 2023 across 16 Russian cities with populations over one million. We used a mix of methods: machine learning (logistic regression) to classify complaints as medical (M-type) or organizational (O-type), statistical tests (chi-square, proportion analysis), and expert validation by nine independent specialists. We also built a novel multidimensional classification of medical practices based on three criteria: diagnostic pathway length, frequency and duration of patient interaction, and whether the work is mainly technical or communicative. Results: Technical specialties received far more medical complaints than communicative ones (39.8% vs. 29.3%, p < 0.001), while communicative specialties received more organizational complaints (45.7% vs. 35.0%, p < 0.001). Specialties that manage chronic conditions over the long term had the highest share of organizational complaints (41.6%). At the city level, the share of communicative specialists correlated negatively with complaints per capita (r = −0.541, p = 0.0306). We found no meaningful gender differences in complaint patterns. Conclusions: The type of medical practice systematically shapes what patients complain about. Technical specialties draw criticism on clinical quality; communicative specialties draw criticism on how care is organized. Long-term care faces challenges rooted more in administrative friction than in clinical competence. These findings show that PREMs, when analyzed through a practice-based lens, can support targeted quality improvement—moving from simply tracking complaints to acting on them in specialty-specific ways. Full article

Ensuring uninterrupted access to essential medicines in public healthcare systems is a persistent challenge with clinical, economic, and environmental implications. Oncology services are particularly vulnerable to stockouts, which compromise therapeutic continuity and increase reliance on urgent procurement with high carbon and waste footprints. This study proposes a risk-informed, data-driven framework for pharmaceutical inventory governance in a high-complexity public oncology hospital in Chile, aligning with sustainability goals and green supply chain principles. Using operational data from 2023–2024, we integrate descriptive analytics, ABC–XYZ segmentation, and a continuous-review (s, Q) policy extended through a Logistic Risk Index (LRI) that consolidates demand variability, supply performance, and clinical-economic criticality. Empirical analysis reveals strong expenditure concentration in AX/AY segments and significant misalignment between institutional and analytically derived parameters. A Monte Carlo simulation N = 1000 runs per scenario) compares baseline, adjusted, and fully risk-informed policies under stochastic demand and lead-time conditions. Results show that the risk-informed configuration reduces stockout exposure by up to 46%, improves fill rates (93.1% → 96.4%), and shortens replenishment delays, while maintaining total logistic cost stability. Critically, urgent orders decrease from 27.4 to 14.8 per year, avoiding an estimated 630 kg CO₂ emissions and 25 kg of packaging waste annually. These findings demonstrate that resilience, efficiency, and sustainability are not competing objectives but can be jointly achieved through integrated analytics and governance. The proposed approach offers a scalable blueprint for public health systems seeking to transition from reactive inventory management toward anticipatory, transparent, and sustainability-oriented decision-making, contributing to SDG 3 (health and well-being) and SDG 12 (responsible consumption and production). Full article

Purpose: This study examines (1) the change in partnership between local health departments (LHDs) and schools of public health or public health programs (SPHs/PHPs) from 2016 to 2019, and (2) the LHD characteristics associated with this partnership. Background: The Council on Education for Public Health updated accreditation criteria in 2016, shifting from core curricula to competencies to better prepare public health graduates for the workforce. Strong partnerships between LHDs and SPHs/PHPs can enhance practical training and employment opportunities for students, ultimately bolstering the public health workforce. Methods: We analyzed the 2016 and 2019 National Profiles of Local Health Departments, using descriptive statistics to evaluate partnership levels and multivariable logistic regression to identify LHD characteristics associated with collaboration. Results: The partnership between LHDs and SPHs/PHPs was suboptimal and unevenly distributed. Engagement in activities like formal training agreements and advisory roles declined. Notably, the presence of formal written agreements for staff training and active recruitment of SPH/PHP graduates by LHDs showed significant improvements (χ² = 3.84; p = 0.049; χ² = 8.19; p = 0.004). Factors such as top executive characteristics, workforce capacity, and governance context influenced these partnerships. Conclusions: The study identifies gaps in LHD engagement with SPHs/PHPs and highlights opportunities for advocacy. Addressing these gaps can lead to a more competent workforce, thereby benefiting both LHDs and SPHs/PHPs in their service to communities. Full article

Traditional palliative care education may limit the development of clinical competencies and attitudes required to alleviate suffering and improve quality of life. Gamification has been proposed as an alternative educational strategy in this field. Background/Objectives: This study aimed to assess the association between gamification-based intervention and palliative care knowledge among nursing students at a public university. Methods: This single-group, pre–post-intervention study was conducted in the Nursing Program of the Universidad Técnica del Norte, Ecuador, including 136 students from the accessible population. Palliative care knowledge was assessed before and after the intervention using the validated Palliative Care Quiz for Nursing (PCQN-SV). Student satisfaction and Moodle usability were assessed using a 10-item Likert-type questionnaire. The gamified educational intervention was delivered online over 60 h. Data were analyzed using descriptive statistics and Wilcoxon signed-rank tests for paired comparisons, and exploratory logistic regression analyses were conducted to evaluate contextual differences across hospitals. Statistical significance was set at α = 0.05. Results: The mean age was 22.9 years (SD = 1.89), and 73.5% were female. Knowledge scores increased significantly after the intervention (Wilcoxon signed-rank test, p < 0.001; r = 0.35). The proportion of students achieving sufficient knowledge (≥13 correct responses) increased from 27.2% (37/136) at baseline to 49.3% (67/136) post-intervention. Contextual analysis indicated variability across clinical training sites, with Lago Agrio showing higher odds of sufficient knowledge (aOR = 3.25; 95% CI [1.26–8.41]; p = 0.015). Conclusions: The gamified intervention was associated with increased palliative care knowledge among nursing students. Heterogeneity across hospitals suggests that contextual factors may influence the magnitude of change. Full article

Introduction: Interpregnancy interval (IPI) plays a critical role in neonatal health, yet optimal spacing remains controversial. This study assessed neonatal outcomes across short and long IPI using three complementary classification approaches to identify consistent patterns of risk. Materials and Methods: In this retrospective cohort study, medical records of 1194 women with a prior live birth who delivered singleton pregnancies in 2024 at a tertiary referral center were analyzed. IPI was calculated as the delivery-to-conception interval (LMP + 14 days). Three IPI classification systems were applied: (1) classical cut-offs (<6, 6–11, 12–23, 24–59, and ≥60 months), (2) quartiles, and (3) tertiles. Primary outcomes included preterm birth, low birth weight (LBW), and NICU admission. Multivariable logistic regression models adjusted for maternal age, gravidity, and previous cesarean delivery. Results: Short IPI (6–11 months) demonstrated the highest NICU admission rates (29.4%). Very long IPI (≥60 months) showed the highest prevalence of LBW (16.6%). Multivariable regression analysis revealed that intervals ≥ 24 months were independently protective against preterm birth (24–59 months: aOR 0.48, p = 0.002; ≥60 months: aOR 0.58, p = 0.042), while maternal age increased preterm birth risk by 7% per year. Short IPI (6–11 months) and very long IPI (≥60 months) independently increased NICU admission risk (aOR 2.29, p = 0.002 and aOR 1.61, p = 0.036, respectively). Previous cesarean delivery was an independent predictor of NICU admission (aOR 1.35; p = 0.048). Conclusions: Short and very long IPIs are associated with increased neonatal morbidity, particularly NICU admission, while the apparent preterm risk in long intervals is largely mediated by maternal age. Once adjusted, IPIs exceeding 24 months demonstrate protective effects against preterm birth. However, the rising trend toward LBW and NICU admission in intervals beyond 5 years suggests that birth-spacing counseling targeting an optimal window of 18–24 months provides the best balance in minimizing competing neonatal risks. Full article

Education occupies an important place among the 17 Sustainable Development Goals. It plays a role in the process of spreading awareness of the concept—its directions, meaning, and goals. According to the idea of the SDG, it is to be universally available to the world’s communities, with the aim of bridging social inequalities, as well as increasing the capacity for responsible functioning and development. The authors of this study believe that knowledge about sustainable development is crucial for shaping social attitudes that determine the uninterrupted development of the world’s economies towards sustainability. In their opinion, it is essential to pay particular attention to ensuring sustainable competences in the education process, which is aimed at preparing staff to perform professional roles in the socio-economic sphere and to be competent in the field of sustainable development. Hence, the aim of this study is to examine the level of awareness of students from selected higher education schools in Poland in this area. The study was conducted on the basis of a diagnostic survey, and the analysis of the results was carried out using qualitative methods, as well as quantitative methods in an in-depth study (logistic regression, supported by PQStat software version 1.8.4.164. The research results indicated that the surveyed students’ knowledge of sustainable development is good, as confirmed by 91% of responses. A key factor in strengthening this knowledge is the educational process implemented as part of their studies (64% of responses). Events supporting the teaching process, such as conferences or meetings with experts, are particularly important for shaping this knowledge. This indicates a high level of student motivation to explore this knowledge and apply it to a model of social behavior, which is rated as responsible by 94% of respondents. In-depth research confirms the above. The odds ratio of 12.994 with a confidence interval of −95% CI: 1.894–+95% CI: 3.238 for the factor of scientific events in the process of supporting green education demonstrates the significance of the findings. Strengthening green education with thematic scientific events is, therefore, an attractive and anticipated form of gaining knowledge on the SDGs by students, and undertaking these events is a recommendation resulting from the presented research. These results are important for modeling sustainable education in terms of the development potential of human resources. Full article

Traffic accidents are among the leading causes of injury worldwide, highlighting the urgent need to better understand the factors that contribute to accident occurrence and severity in order to improve road safety and reduce injuries and fatalities. This study analyzes the U.S. Accidents dataset, comprising data collected from 2016 to 2023, to identify the key determinants of accident severity and to evaluate feature-selection techniques for predictive modeling. To this end, several feature-selection methods are examined, including L1-regularized logistic regression, minimum redundancy maximum relevance (mRMR), conditional mutual information maximization (CMIM), ReliefF, and tree-based importance measures; these are compared with the Weighted Conditional Mutual Information (WCFR). The selected feature subsets are then evaluated using three machine learning models: logistic regression, random forest, and XGBoost. Experimental results show that WCFR consistently outperforms the competing methods, achieving higher validation accuracy (up to approximately 0.84) and Macro-F1 scores (up to approximately 0.55), while using fewer features and maintaining model interpretability. These results indicate that WCFR is particularly effective for accident severity modeling and highlight its potential as a robust feature selection strategy for large-scale transportation safety analytics and future severity prediction studies. Full article

Australian regional communities are actively seeking development pathways that generate local economic value while maintaining environmental and cultural integrity. In this context, GeoRegions have emerged in Australia as a community-led approach for recognising and interpreting geoheritage and associated abiotic–biotic–cultural (ABC) values through geotourism and geoeducation. The GeoRegion concept remains intentionally operationally flexible, but for regional communities encountering a myriad of barriers to sustainable geotourism implementation, any uncertainty for proponents about what constitutes an implementable GeoRegion and what resources and governance arrangements are required for credible and sustained delivery requires resolution. This study developed a stakeholder-informed conceptual model to clarify the practical ‘building blocks’ of GeoRegion establishment and the conditions under which GeoRegions can contribute to sustainability-oriented regional development. Using a design thinking framing and semi-structured interviews with thirteen expert participants, we used semantic discourse analysis to identify the factors perceived as essential to GeoRegion viability and legitimacy. We found that participants expected GeoRegions to be geologically centred, but their perceived value and long-term durability depend on (i) genuine community support and locally legitimate narratives (including Indigenous knowledge where appropriate), (ii) capable champions or coordinating groups, (iii) sustained resourcing for interpretation and visitor readiness, and (iv) a facilitative and not prescriptive role for government. Participants emphasised that GeoRegions should never be constrained by land tenure but cautioned that competing land uses, access logistics and uneven capacity across regions were highly influential in the delineation of feasible boundaries and management intensity. Our GeoRegion model differentiates core inputs (community mandate, knowledge co-production, geoheritage significance, human capacity and funding) from expected outputs (interpretive materials, geoeducation, geotourism, economic development, conservation outcomes and strengthened place identity), and we identify feedback that can either reinforce or erode sustainability outcomes over time. We argue that GeoRegions can provide a low-risk, scalable mechanism for geoconservation-informed regional development, particularly where formal protected-area tools or geopark ambitions are politically or economically constrained, provided that supporting governance and resourcing are treated as essential design requirements. Full article

Background/Objectives: Early manifestations of body dissatisfaction and subclinical disordered eating are increasingly recognized as important correlates of later disordered eating, underscoring the need for research in preadolescent populations. This study examined the prevalence of disordered eating risk and tested whether body-figure discrepancy and thinness-related attribution patterns were associated with disordered-eating symptomatology beyond gender and BMI among children aged 10–12 years. Methods: A total of 227 children completed the Children’s Eating Attitudes Test-26 (ChEAT-26), body-figure silhouette measures, and assessments of thinness-related social-emotional, negative, and competence attributions. Results: Overall, 16.3% of participants scored at or above the clinical cutoff for elevated eating-pathology risk, with no significant gender differences. Hierarchical regression analyses showed that the discrepancy between perceived and ideal body figure was significantly associated with disordered eating severity, although the proportion of explained variance was modest. Logistic regression further indicated that each unit increase in body-figure discrepancy was associated with a 37% increase in the likelihood of exceeding the clinical risk threshold. In contrast, thinness-related attribution indices were intercorrelated but were not significantly associated with disordered eating, nor were their effects moderated by gender. Conclusions: These findings indicate that body-image discrepancy is associated with disordered eating in late childhood, at a stage when marked gender differences are not yet evident. Although causal inferences cannot be drawn, the presence of clinically elevated symptom levels and the observed associations highlight the potential importance of early attention to body dissatisfaction in preventive efforts. Full article

The rapid development of artificial intelligence (AI) has intensified debates regarding its impact on the labor market, specifically concerning the potential for replacement versus the augmentation of human labor. While the existing literature highlights both the opportunities and risks associated with AI, research conducted by faculty in academic settings focuses predominantly on academic integrity, paying limited attention to AI readiness and/or anxiety related to labor market entry. This study aims to compare the perceptions of students and faculty in Romania regarding the impact of AI on employment, exploring the role of personal and organizational readiness in shaping these attitudes. The research is based on an empirical approach utilizing a questionnaire applied to a sample of 271 respondents, consisting of 197 students and 74 faculty members. Data analysis included descriptive and inferential methods, such as Chi-square tests and binary logistic regression, and was theoretically grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT). The results indicate significant differences between students and faculty regarding general attitudes toward AI, with students manifesting higher levels of concern regarding job replacement. However, both groups converge in their functional definition of AI as a major factor in labor transformation, suggesting an evaluative rather than a cognitive difference. Multivariate analyses show that personal readiness and the perception of organizational readiness are the primary predictors of a positive attitude toward AI, while demographic variables lose statistical significance when these dimensions are controlled. This study contributes to the literature by highlighting that AI-related anxiety is not inherently determined by demographic characteristics but represents a malleable state shaped by individual competencies and institutional conditions. The findings underscore the strategic role of universities in reducing perceptions of replacement and facilitating the transition to an AI-augmented labor market through training policies, adequate infrastructure, and transparent institutional communication. Full article

Systems thinking is a core competence in logistics management, as decisions across transportation, warehousing, and delivery functions are highly interconnected and often generate delayed, trade-off, or system-wide consequences. Despite the increasing integration of generative artificial intelligence (GenAI) tools into logistics education, limited research has examined how to enhance systems thinking in such contexts. Drawing on triadic reciprocal determinism, this study conceptualizes systems thinking enhancement as an emergent outcome of interactions among behavioral regulation, cognitive conditions, and environmental scaffolding. Using survey data from 236 logistics management students in Chinese universities, we integrate Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine both net effects and configurational mechanisms. Results show that self-regulated learning exhibits the strongest positive association with systems thinking, while germane cognitive load is positively associated and extraneous cognitive load is negatively associated with systems thinking. Teacher GenAI scaffolding is linked to more favorable cognitive load allocation. fsQCA findings further reveal that high-level systems thinking emerges from specific combinations where self-regulated learning and germane cognitive load are fundamental conditions, whereas the absence of self-regulated learning consistently leads to low-level systems thinking. These findings provide guidance for the design of GenAI-supported curricula and scaffolding strategies. Full article

The prediction of material lifetime is central to nanomaterial engineering and reliability analysis. We propose the Marshall–Olkin Power Half-Logistic (MOPHL) distribution, obtained by applying a Marshall–Olkin transform to the Power Half-Logistic baseline. We derive core properties—including moments, hazard rate characterization, and Rényi entropy—and develop inference under generalized progressive hybrid censoring. Estimation is carried out via maximum likelihood and Bayesian methods using a Metropolis–Hastings sampler. Asymptotic results, Fisher information, and corresponding confidence/credible intervals are provided. A Monte Carlo study assesses bias, the mean squared error, and coverage across censoring scenarios and hazard regimes. In a case study on hydroxylated fullerene degradation, MOPHL outperforms nine competing models in goodness-of-fit and predictive reliability. We also report the mean time to failure and mean residual life to support engineering decision-making. The proposed framework offers a tractable and robust tool for degradation analysis under censored data, with applicability to materials, mechanical components, biomedical devices, and environmental monitoring. Full article