Search Results (191)

The transition toward Industry 5.0 in sustainable construction demands a radical reconceptualisation of workforce development, moving beyond purely technical training to embrace human-centricity, digitalisation, green competencies, and socio-cognitive resilience. Traditional vocational and higher education systems have largely failed to bridge the gap between emerging construction industry demands and the competencies possessed by current and future professionals. This systematic review investigates how Learning Factories’ 5.0 immersive, experiential, and technology-rich educational environments can address these gaps in sustainable construction contexts. Drawing on a synthesis of 71 peer-reviewed publications spanning 2015–2026 and supplemented by targeted construction-domain literature, this study pursues three objectives: (1) identifying core competencies for Industry 5.0 readiness in sustainable construction, (2) examining how Learning Factories 5.0 support the development of these competencies, and (3) proposing a competency-driven framework for integrating Learning Factories 5.0 into sustainable construction education and training. Seven transdisciplinary competency clusters are identified—Attitude toward Digitalisation, Technical–Green Proficiency, Information and Data Literacy, Digital Security, Collaborative Systems Thinking, Adaptive Problem-Solving, and Reflective Sustainability Practice—and a theoretically derived, eight-phase Construction Learning Factory 5.0 (CLF5.0) Framework is proposed as a conceptual architecture for future empirical development and institutional adaptation. The framework is presented as a generative starting point rather than a prescriptive model, and its effectiveness in diverse construction education contexts requires empirical validation through future implementation studies. Findings reveal that while Learning Factories offer transformative potential, critical barriers remain in terms of economic feasibility, faculty development, industry–academia alignment, and empirical validation. This paper contributes a construction-specific competency architecture and implementation pathway to support the industry’s transition toward a sustainable, human-centric, and Industry 5.0-aligned future. Full article

This study examines labor market dynamics in Kazakhstan in the context of digital transformation, human capital development, and workforce adaptation. It focuses on unemployment trends, demographic changes, and structural labor market characteristics between 2010 and 2025. Several time-series forecasting approaches were evaluated to assess future unemployment trends. Among the tested models, SARIMA demonstrated the best forecasting performance and was used to estimate unemployment dynamics through 2028. The results indicate a relatively stable labor market, with a gradual decline in unemployment over the forecast period. The analysis also shows that demographic structure, youth labor market integration, migration processes, and educational attainment play important roles in shaping employment outcomes. Higher education is associated with lower unemployment, while vocational groups demonstrate greater labor market vulnerability. The study contributes by combining unemployment forecasting with demographic and workforce adaptation analysis in the context of an emerging economy. The findings suggest that workforce adaptability, digital skills development, and targeted employment policies may support sustainable labor market development under conditions of technological transformation. Full article

The current study aimed to investigate complex links among a large set of anxiety-related variables and identify targets for well-being interventions in a large sample of male and female vocational education training students. In total, 28 psychological constructs, such as self-esteem, parental pressure and dissatisfaction and motivation, were assessed in four groups of VET students (mode age: 16). The sample included 3069 females in ASSP schools (nursing and caring); 2108 females and 1772 males in Commerce schools (sales and management); and 2262 males in MELEC schools (electricity and maintenance). We used Gaussian Graphical models (GGMs) that allow for building sparse models of links among multiple variables and detecting targets for interventions via the identification of the most central nodes. We showed gender differences in absolute means for some variables (higher self-esteem and math grades in males; higher anxiety and error sensitivity, but stronger endorsement of mastery approach achievement goals in females), as well as in network structure. GGMs suggested that the key nodes were self-reported math competence for females in the ASSP group, self-regulation for females in Commerce, and mastery approach goals for males in both MELEC and Commerce groups, and that these should be differentially targeted by educational interventions in these populations. Full article

This study recognizes education as an investment and estimates the private rates of return to upper secondary education in Greece, overall, by type (general or vocational) and by gender. Earnings data were collected through primary research using stratified sampling from the private sector of the economy. The analysis is based on the Mincer method and is complemented by machine learning methods, including Support Vector Regression, Random Forests, and Extreme Gradient Boosting. The empirical analysis shows that investing in upper secondary education (general and vocational) is profitable. The private rates of return in upper general secondary education are higher than those in vocational education, and female graduates exhibit higher returns than male graduates. Machine learning models achieve modest improvements in predictive performance, as reflected in higher adj. R² values and lower prediction errors. However, the estimated rates of return remain broadly consistent with those obtained from the Mincer method. This convergence suggests that the Mincer specification captures the core structural relationship between education and earnings, while machine learning models primarily enhance predictive accuracy without substantially altering the estimated economic returns. This finding highlights the robustness of the traditional econometric framework and clarifies the complementary role of machine learning techniques in empirical labor economics. Full article

Vocational high school students represent a substantial yet understudied population in school-based mental health research. Drawing on positive psychology and bioecological theory, this study examined whether personal growth initiative (PGI) shows a statistical indirect effect with respect to the relationships between multidimensional school climate and mental health outcomes among Chinese vocational students. Participants were 14,006 students from 112 vocational high schools. Two-level path models simultaneously entered different climate dimensions to estimate their unique associations with PGI, depressive symptoms, and Subjective well-being (SWB) at the within- and between-school levels, controlling for gender and socioeconomic status. Within schools, Safety, Interpersonal Relationships, Rules and Norms/Career Development Support, and Teaching and Learning/Diversity were positively associated with PGI, which in turn was associated with lower depressive symptoms and higher SWB. Wald tests indicated that Safety showed the strongest overall association with depressive symptoms, whereas Interpersonal Relationships showed the strongest overall association with SWB. At the between-school level, school-average climate and school-average PGI were associated with both outcomes, although these findings should be interpreted cautiously given the limited between-school power and substantial overlap among aggregated climate indicators. Overall, the findings are consistent with PGI being an important student-level pathway linking school climate to mental health in vocational education. Full article

Operationally, energy transition takes place at the local level, that is, in cities and rural municipalities. Its effectiveness is, therefore, dependent on individual actions undertaken in enterprises and households. It also constitutes a particularly challenging task for industrial regions with centuries-old mining traditions. Meanwhile, the opinions of residents living in mining cities receive little attention in the literature. For these reasons, this study used survey research conducted in 19 Silesian cities with county rights and on a representative sample of 1863 residents. In this way, answers were sought to the following research questions: (1) How do urban residents in a developing economy in a post-mining region assess their knowledge regarding environmental protection and energy transition? (2) How do they evaluate local authorities’ actions concerning the replacement of non-ecological heating sources in households? The analysis of results employed descriptive statistics and non-parametric statistical tests, identifying differences in respondents’ assessments according to gender, age, education, and place of residence. The analyses conducted indicate that residents assess their environmental awareness as average. They also rate their knowledge of the energy transition below average, despite being in the midst of it. The assessments of men, older individuals, and those with vocational and secondary education are higher in both cases than the assessments of women, younger generations, and respondents with primary, post-secondary, and higher education. Respondents also rate financial and informational–educational support for heating source replacement as average. Importantly, however, these actions are noticed and appreciated. They meet the expectations of less formally educated individuals (formal education level: primary, vocational, and secondary). However, they do not generate enthusiasm among those with post-secondary and higher education, whose environmental needs and expectations may be higher. The level of financial support, and to a lesser extent informational–educational support, differs significantly among the studied cities, indicating the absence of a coherent regional policy. This may also result in deepening environmental disparities and inequalities in quality of life among the studied urban centers. The two-dimensional assessment reveals that the majority of the examined cities fall into the stagnator category, exhibiting average levels of both environmental awareness and institutional support for energy transition. The most favorable prospects for effective energy transition are observed in Gliwice and Żory, while Zabrze, Świętochłowice, and Jastrzębie-Zdrój—post-mining cities burdened by limited development potential and financial constraints—demonstrate the least promising outlook. The conclusions and recommendations derived from this article directly align with the implementation of Sustainable Development Goal 7—Affordable and Clean Energy (SDG 7)—which addresses energy transition, including the adoption of clean heat sources. They also support the development of sustainable cities, thereby contributing to the achievement of Sustainable Development Goal 11—Sustainable Cities and Communities (SDG 11). Full article

Background/Objectives: Climate change and environmental degradation pose increasing public health threats, while healthcare systems significantly contribute through high energy use, water consumption, and waste generation. In hospital settings, healthcare technicians play a critical role, as their routine practices directly influence environmental sustainability. Despite this central role, healthcare technicians remain an under-recognized group in sustainability research and health policy. Green hospital practices therefore constitute a key public health strategy, requiring strategic management attention to this operational workforce. This study aimed to assess green hospital awareness among healthcare technician students in Türkiye and to examine associated sociodemographic factors from a public health perspective. Methods: This descriptive cross-sectional study included 313 students enrolled in health services vocational programs who were receiving hospital-based practical training. Data were collected using a personal information form and the validated Green Hospital Awareness Scale. Descriptive statistics, independent t-tests, one-way ANOVA, and Spearman correlation analyses were conducted using SPSS version 26.0, with statistical significance set at p < 0.05. Results: Although most participants reported no prior knowledge of the green hospital concept, overall awareness levels were moderate to high. The highest mean scores were observed in indoor environmental quality and materials–resources subdimensions, whereas water efficiency scores were the lowest. Female students demonstrated significantly higher total awareness and materials–resources scores (p < 0.05). No significant differences were observed by age or academic department. Conclusions: Healthcare technician students exhibit measurable green hospital awareness; however, important gaps remain in resource- and infrastructure-related sustainability domains. Strengthening educational and policy initiatives targeting this often-overlooked yet operationally essential workforce may improve environmentally responsible practices, enhance resource efficiency, and support sustainable healthcare systems and population health. Full article

This study examined the policies governing the teaching workforce in China’s higher vocational education system. We developed a two-dimensional analytical framework (“policy content elements–policy tools”) to conduct an in-depth analysis of 124 central and provincial policy texts. The key findings are as follows: (1) Imbalance in policy tools: Authoritative and capacity-building tools dominate, while symbolic and exhortative tools are underutilized. Disparities exist between the central and provincial policies regarding the deployment of specific tools. (2) Prioritization of content elements: The strongest emphasis is placed on teacher cultivation, followed by teacher evaluation and safeguarding. Policies concerning teacher recruitment (access) have received little attention. (3) Policy misalignment: Poor coordination between policy tools and content elements undermines overall policy effectiveness. To address these issues, we propose the following: (1) Optimizing the policy tool portfolios: Reduce overreliance on authoritative tools for teacher recruitment and strengthen the use of incentive-based and capacity-building tools for evaluation and safeguards. (2) Strengthening recruitment policies: Formalize qualification standards, rigorously enforce teaching certifications, and standardize hiring procedures. (3) Enhancing policy coordination: Incorporating regional variations to improve the evidence-based integration of policy tools. These recommendations aim to refine the teaching workforce policies and advance the high-quality development in higher vocational education. Full article

Taiwan’s higher education system is undergoing profound demographic change as declining birthrates continue to reduce the size of incoming student cohorts. These demographic pressures pose significant challenges for institutional governance, enrollment management, and program sustainability, particularly within the technological and vocational education and training (TVET) sector, which relies heavily on stable freshman enrollment. Using five nationwide administrative datasets, this study conducts a longitudinal analysis of 1087 departmental restructuring cases implemented across 74 TVET institutions between 2020 and 2024. The restructuring strategies examined include renaming, mergers, additions, closures, and quota expansions. Fixed-effects regression models and multi-year enrollment trend analyses are employed to evaluate how different forms of departmental adjustment influence freshman enrollment outcomes. The results show that departmental renaming is the only strategy consistently associated with improved enrollment performance, particularly in private institutions. In contrast, mergers generally fail to generate positive enrollment effects, while closures are concentrated among programs with sustained low enrollment over multiple years. Newly established programs and quota expansions perform relatively well in fields aligned with strong labor market demand, such as healthcare and applied technologies. By providing large-scale, system-level empirical evidence, this study contributes to research on higher education policy, institutional governance, and enrollment management under demographic decline. The findings offer practical implications for administrators and policymakers seeking to design effective program adjustment strategies and support institutional resilience in higher education systems facing long-term population contraction. Full article

Sustainable vocational training refers to the continuous and timely provision of qualified personnel to support a country’s economic growth and development. Recognizing the significance of these activities is crucial, as they play a key role in national progress. In recent years, research on the sustainability of vocational education systems across different countries has gained momentum. This study aims to identify the key components that ensure the sustainability and effectiveness of the vocational education system in Turkey. Data were collected from 2450 academicians employed in higher vocational schools across the country. Survey data were analyzed, and hypotheses were tested using Structural Equation Modeling (SEM). The findings reveal four main components essential for sustainable vocational education in Turkey: (1) enhancing digitalization and access opportunities, (2) increasing awareness of energy and resource efficiency, (3) integrating data-driven education policies and (4) online education systems into vocational curricula. Implementing these components in all vocational institutions is considered vital for advancing Turkey’s economic and developmental objectives. Full article

Background/Objectives: Global disability scales such as the Glasgow Outcome Scale-Extended (GOS-E) may not fully capture real-life participation after traumatic brain injury (TBI). This secondary analysis quantified mismatch between global disability and participation 5 years after moderate-to-severe TBI and identified predictors of a “good recovery, poor participation” profile. Methods: We analysed the TBIMS National Database Public Use Data Set, including adults ≥ 16 years with moderate-to-severe TBI, 5-year follow-up, and valid GOS-E, PART-O, and employment data. High versus low global outcome was defined as GOS-E 7–8 versus 3–6; good versus poor participation was defined using PART-O total (≥median vs. ≤25th percentile) plus productive role engagement. Four outcome profiles were derived and compared using 2 × 2 factorial analyses and regression. Results: The analytic cohort included 6363 participants; among those with high GOS-E, 16.8% met criteria for poor participation. Profiles with poor participation showed lower participation and lower life satisfaction and higher mood symptoms than Group A (high GOS-E, good participation), whereas those with low GOS-E but good participation showed preserved participation with greater emotional burden. Older age, lower education, minority race/ethnicity, pre-injury unemployment or retirement, longer post-traumatic amnesia, and lower 2-year GOS-E independently predicted mismatch. Sensitivity analyses using alternative GOS-E and participation cut-offs and life-satisfaction outcomes yielded similar patterns. Conclusions: Five years after moderate-to-severe TBI, good global recovery does not guarantee successful reintegration, and some individuals maintain participation despite persisting disability. Routine assessment of participation and life satisfaction alongside global disability is needed to identify high-risk profiles and target vocational and psychosocial interventions. Full article

This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. Following PRISMA 2020 guidelines, 27 peer-reviewed empirical studies published between 2010 and 2024 were identified from major international and Chinese databases and included in the analysis. Using a random-effects model, the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72), alongside substantial heterogeneity across study designs, outcome measures, and implementation contexts. Subgroup analyses suggest variation across regional and institutional settings, which should be interpreted cautiously given small sample sizes and diverse methodological approaches. Based on the synthesized evidence, the study proposes a conceptual AI-supported skill assessment framework that distinguishes empirically grounded components from forward-looking extensions related to generative AI. Rather than offering prescriptive solutions, the framework provides an evidence-informed baseline to support future research, system design, and responsible integration of generative AI in higher education assessment. Overall, the findings highlight both the potential and the current empirical limitations of AI-enabled assessment, underscoring the need for more robust, theory-informed, and transparent studies as generative AI applications continue to evolve. Full article

The rapid expansion of private universities in the past a few decades has created a unique sector in Chinese higher education system. Unlike public research-oriented institutions, Chinese private universities are tuition-dependent, resource-constrained, and primarily vocation-oriented. Lacking the prestige of academics, the campus physical environment in these institutions becomes a key strategic asset for student recruitment, retention, and performance. However, academic research addressing these contexts remains scarce. This study aims to develop a reliable measurement tool—the University Campus Environment Satisfaction Scale (UCESS)—specifically tailored to assess student satisfaction with the physical environment in Chinese private universities. Based on 1050 valid questionnaires from 4 representative universities in Guangdong province, exploratory and confirmatory factor analyses revealed a hierarchical structure comprising 10 first-order factors and 3 second-order dimensions: (1) Safety and accessibility; (2) Core living and learning environment; and (3) Developmental and amenity resources. The findings reveal that students in Chinese private universities prioritize tangible living, teaching and safety conditions over higher-level developmental amenities, reflecting a layered satisfaction logic. Furthermore, this study demonstrates the differentially weighted relationships between campus elements and overall campus satisfaction, providing administrators with a scientific diagnostic tool to optimize resource allocation and implement student-centered planning strategies. Full article

Within the global sustainable development agenda, Sustainable Development Goal 4 (SDG 4) highlights improving the accessibility, quality, and learning experience of technical and vocational education and training (TVET). In China, students in vocational colleges often face greater disparities in academic preparation and access to educational resources than their peers in general higher education. Although artificial intelligence (AI) can provide additional learning support and help mitigate such inequalities, there is little empirical evidence on whether and how Gen-AI usage is associated with vocational students’ learning experiences and emotional outcomes, particularly academic anxiety. This study examines how Gen-AI usage is related to academic anxiety among Chinese vocational college students and explores the roles of class engagement and teacher support in this relationship. Drawing on Conservation of Resources (COR) theory, we analyse survey data from 511 students using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results indicate that Gen-AI usage is associated with lower academic anxiety, with class engagement mediating this relationship. Teacher support for Gen-AI usage positively moderates the association between Gen-AI usage and class engagement. The fsQCA results further identify several configurations of conditions leading to low academic anxiety. These findings underscore AI’s potential to enhance learning quality and experiences in TVET and provide empirical support for advancing SDG 4 in vocational education contexts. Full article

Introduction: Personality disorders are enduring, maladaptive patterns that impair social and vocational functioning. The DSM-5 Alternative Model for Personality Disorders (AMPD) distinguishes Criterion A (personality functioning: identity, self-direction, empathy, intimacy) from Criterion B (maladaptive trait domains: negative affectivity, detachment, antagonism, disinhibition, psychoticism). We frame this study within Criterion B, supporting the use of a dimensional approach that complements (rather than replaces) normative models like the Five-Factor Model (FFM) and addresses cross-cultural gaps amid Saudi Arabia’s rapid sociocultural change such as the reforms associated with Vision 2030. Given Saudi Arabia’s collectivist orientation and evolving sociocultural norms under Vision 2030, the dimensional approach of the AMPD Criterion B offers a culturally sensitive lens for capturing personality pathology beyond Western-centric diagnostic models. Aim: We aimed to examine how PID-5-BF maladaptive trait domains vary across key sociodemographic factors in Saudi adults. Subjects and Methods: This was a quantitative, cross-sectional analytical study conducted among Saudi adults using the PID-5-BF Convenience sampling was performed via the dissemination of an online survey; we aimed for 377 participants and obtained 343 completed responses (~91% of the target sample). For trait assessment, we used the PID-5-BF; analyses compared domains across sociodemographic groups. Results: Females showed a higher negative affect; participants ≤ 30 years exhibited higher psychoticism than those >40; and single individuals reported lower detachment and psychoticism than their married peers. Conclusions: Gender, age, and marital status are associated with differences in maladaptive trait expression, supporting the need for culturally tailored screening and interventions in Saudi mental health services. These findings should be interpreted with caution given the fact that WhatsApp-based convenience sampling was used, which may bias the results as the respondents were more likely to live in urban areas, be educated, and be technologically proficient. Full article