Search Results (418)

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-like symptoms. Routine cleaning practices, which often involve the use of strong chemical agents to maintain hygienic settings, have been shown to contribute to respiratory issues. Laboratories, where chemicals such as hydrochloric acid and ammonia are frequently used, represent an underexplored context in the study of occupational asthma. While much of the research on chemical exposure has focused on industrial and high-risk occupations or large cohort populations, less attention has been given to the risks in laboratory and medical environments, particularly for professional cleaning staff. Given the growing reliance on cleaning agents to maintain sterile and safe workspaces in scientific research and healthcare facilities, this gap is concerning. This study developed an exploratory simulation tool, using a simulated cohort based on key demographic and exposure patterns from foundational research, to assess the impact of chemical exposure from cleaning products in laboratory environments. Four supervised machine learning models were applied to evaluate the relationship between chemical exposures and asthma-like symptoms: (1) Decision Trees, (2) Random Forest, (3) Gradient Boosting, and (4) XGBoost. High exposures to hydrochloric acid and ammonia were found to be significantly associated with asthma-like symptoms, and workplace type also played a critical role in determining asthma risk. This research provides a data-driven framework for assessing and predicting asthma-like symptoms in professional cleaning workers exposed to cleaning agents and highlights the potential for integrating predictive modeling into occupational health and safety monitoring. Future work should explore dose–response relationships and the temporal dynamics of chemical exposure to further refine these models and improve understanding of long-term health risks. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Posture, defined as the body’s alignment relative to gravity, plays a vital role in musculoskeletal health by influencing muscle efficiency, joint integrity, and overall balance. The global shift to remote and sedentary work environments during the COVID-19 pandemic has amplified concerns regarding posture-related disorders and long-term ergonomic risks. This study introduces ALIGN, an IoT-enabled intelligent system for real-time sitting posture detection that integrates both machine learning and deep learning methodologies. Implemented on a single-board computer, the system processes live video streams to classify user posture as correct or incorrect and provides alert-based notifications when sustained improper posture is detected, thereby supporting real-time posture awareness without issuing corrective instructions. Among conventional classifiers, K-Nearest Neighbors (KNN), Support Vector Classifiers (SVC), and Multi-Layer Perceptrons (MLP) achieved accuracies of 98.74%, 96.64%, and 97.17%, respectively, while in the deep learning category, ResNet52 reached a test accuracy of 94.37%, outperforming DenseNet121 (81.53%). By enabling intelligent real-time detection and monitoring, ALIGN offers a scalable and cost-effective solution for ergonomic risk awareness and preventive digital health support. Full article

Stress in the workplace has been recognised by the World Health Organization (WHO) as a global health epidemic. Research examining the most stressful industries to work in the UK consistently ranks education among the highest groups, encompassing early years practitioners to higher education academics. One of the most commonly reported contributory factors is poor work–life balance, with high levels of emotional exhaustion and depersonalisation—key components of burnout—endemic. Related research has highlighted unprecedented mental health difficulties among children and young people; while many educators feel ill-equipped to manage the levels of mental distress they encounter in the classroom and playground on a daily basis, contributing to their own diminished wellbeing. The current author posits that at the heart of a well-functioning learning environment is the holistic wellbeing of every member of the education community. This paper brings together evidence from across different levels of education to expose systemic failures to address work-related stressors, highlighting gaps in effective support mechanisms to meet the needs of both learners and educators. Philosophical questions concerning professional identities and the function of a contemporary education system with mental health on its agenda are considered. Finally, recommendations are put forward to help tackle the current crisis and curb the exodus of professionals from across the sector. Full article

The research elaborates on and empirically verifies an integrative model that describes how the combination of various workplace resources results in the improvement of employee and organizational outcomes. It is based on the Job Demands–Resources model and the Resource-Based View to conceptualize Employee Experience Capital (EEC) as a higher-order construct, consisting of seven interrelation drivers, including digital autonomy, inclusive cognition, sustainability alignment, AI synergy, mindful design, learning agility, and wellness technology. This study examines the effect of these resources in developing two psychological processes, work resonance and employee vitality, which subsequently improves organizational performance. It also examines how the well-being of employees can be a contextual moderator that determines such relationships. The study, based on a cross-sectional design and the diversified sample of the employees who work in various digitally transformed industries, proves that EEC is a great way to improve resonance and vitality, which are mutually complementary mediators between resource bundles and performance outcomes. Employee well-being turns out to be a factor of performance, as opposed to a circumscribed condition. The results put EEC as one of the strategic types of human capital that values digital, sustainable, and wellness-oriented practices to employee well-being and sustainable organizational performance and provides new theoretical contributions and practical guidance to leaders striving to create resource-rich, high-performing workplaces. Full article

Emerging and re-emerging viruses continue to pose major threats to public health. Their ability to adapt, cross species barriers, and spread rapidly can trigger severe outbreaks or even pandemics. Strengthening preparedness with comprehensive and efficient strategies is therefore essential. Here, we explore the key components of viral outbreak preparedness, including surveillance systems, diagnostic capacity, prevention and control measures, non-pharmaceutical interventions, antiviral therapeutics, and research and development. We emphasize the increasing importance of genomic surveillance, wastewater-based surveillance, real-time data sharing, and the One Health approach to better anticipate zoonotic spillovers. Current challenges and future directions are also discussed. Effective preparedness requires transparent risk communication and equitable access to diagnostics, vaccines, and therapeutics. The COVID-19 pandemic highlighted both the promise of next-generation vaccine platforms and the necessity of maintaining diagnostic capacity, as early testing delays hindered containment efforts. Countries adopted various non-pharmaceutical interventions: risk communication and social distancing proved to be the most effective, while combined workplace infection-prevention measures outperformed single strategies. These experiences highlight the importance of early detection, rapid response, and multisectoral collaboration in mitigating the impact of viral outbreaks. By applying best practices and lessons learned from recent events, global health systems can strengthen resilience and improve readiness for future viral threats. Full article

This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday environments. To overcome these limitations, we propose a comprehensive solution that utilizes only single-camera 2D images. Our pipeline comprises four distinct stages: (1) extraction of 2D human pose keypoints for multiple persons using a pretrained Human Pose Estimation model; (2) a novel ensemble learning model for person-view classification—distinguishing between front, back, and side perspectives—which is critical for accurate subsequent analysis; (3) a view-specific module that calculates body-segment angles, robustly handling movement pairs (e.g., flexion–extension) and mirrored joints; and (4) a pose assessment module that evaluates calculated angles against established biomechanical Range of Motion (ROM) standards to detect potentially injurious movements. Evaluated on a custom dataset of high-risk poses and diverse images, the end-to-end pipeline demonstrated an 87% success rate in identifying dangerous postures. The view classification stage, a key contribution of this work, achieved a 90% overall accuracy. The system delivers individualized, joint-specific feedback, offering a scalable and deployable solution for enhancing human health and safety in various settings, from home environments to workplaces, without the need for specialized equipment. Full article

Background/Objectives: The Manchester Colour Wheel (MCW) was developed as an alternative way of assessing health status, mood and treatment outcomes. There has been a dearth of research on this alternative assessment approach. The present study examines the sensitivity of the MCW to pain, psychological factors and recovery status in 1098 people with insured injuries treated in an interdisciplinary clinic. Methods: A deidentified data set of clients treated in a multidisciplinary clinic was conveyed to the researchers, containing results of MCW and injury-specific psychometric tests at intake, as well as recovery status at discharge. Systematic machine modelling was applied. Results: There were no significant differences between the four injury types studied: motor crash-related Whiplash Associated Disorder (WAD) and workplace-related Shoulder Injury (SI), Back Injury (BI) and Neck Injury (NI) on the MCW. Augmenting the MCW with Machine Learning (ML) models showed overall classification rates for Classification and Regression Tree (CRT) of 75.6% for Anxiety, 70.3% classified for Depression and 68.5% for Stress, and Quick Unbiased Efficient Statistical Trees could identify 68.5% of Pain Catastrophisation and 62.7% of Kinesiophobia. Combining MCW with psychometric measurements markedly increased the predictive power, with a CRT model predicting WAD recovery status with 80.7% accuracy, SI recovery status 81.7% accuracy and BI recovery status with 78% accuracy. A Naïve Bayes Classifier predicted recovery status in NI with 96.4% accuracy. However, this likely represents overfitting. Conclusions: Overall, MCW augmented with ML offers a promising alternative to questionnaires, and the MCW appears to measure some unique psychological features that contribute to recovery from injury. Full article

Background/Objectives. ICD–11 classifies burnout as a work-related issue arising from chronic workplace stress that has not been successfully managed. According to the Job Demands/Resources Model, job demands represent sources of stress and job resources may buffer the impact of job demands on job strain. Since every profession has its specific spectre of work demands/resources related to stress development, the aim of this study was to examine a model predicting workplace burnout dimensions (emotional exhaustion—EE, depersonalisation—DP, personal accomplishment—PA) in special educational needs (SEN) and general education (GE) teachers, with job demands representing potential “risk factors” and job resources potential “protective factors”. Methods. The study involved 116 SEN teachers from eight primary schools for children with learning difficulties and a sample of 145 teachers from general primary schools in the Belgrade region, which was balanced according to the representation of the main demographic variables in the SEN group. The Maslach Burnout Inventory and Job Characteristics Questionnaire were the instruments employed. Results. No difference was found between SEN and GE teachers in the intensity of burnout dimensions. In the SEN group, Changes were the predictors of all three burnout dimensions, Work environment for EE and DP, Emotional demands and Support from colleagues for EE, Cognitive/Quantitative for PA, and Job control for PA. Concerning the GE group, Support from colleagues predicted all three dimensions, Job control EE and DP, Cognitive/Quantitative DP and PA, Changes DP, and Role conflict and Seniority EE. Conclusions. The results of the study provide a foundation for further testing of a hypothetical predictive model of burnout with job demands as direct predictor and job resources as mediators of this relation. Full article

Background: Unsafe care remains a major global health challenge, contributing to millions of preventable deaths and ranking among the top ten causes of mortality and disability worldwide. The World Health Organization’s Global Patient Safety Action Plan 2021–2030 emphasizes the need for strong leadership and system-wide engagement to eliminate avoidable harm. As the largest component of the global healthcare workforce, nurses—especially those in management roles—are essential to achieving these goals. Objective: This narrative review synthesizes global evidence on how nursing management practices, particularly leadership, staffing, and safety culture, influence patient safety outcomes across diverse health systems. Methods: A purposive narrative review was conducted using PubMed, CINAHL, Scopus, and Web of Science databases. Peer-reviewed studies and organizational reports published between 2020 and 2025 were evaluated. A thematic synthesis approach was used to identify patterns related to leadership style, staffing ratios, workplace conditions, and organizational resilience. Quality appraisal followed adapted Critical Appraisal Skills Programme (CASP) and Joanna Briggs Institute (JBI) guidance. Results: A total of 37 peer-reviewed empirical studies were included in the narrative synthesis, along with key global policy and foundational framework documents used to contextualize findings. Evidence consistently demonstrated that transformational leadership, adequate nurse staffing, positive safety culture, and organizational learning structures are strongly associated with improved patient outcomes, reduced errors, and enhanced workforce well-being. Most studies exhibited moderate to high methodological rigor. Conclusions: Nursing management plays a decisive role in advancing global patient safety. Policies that strengthen leadership capacity, ensure safe staffing, promote just culture, and support nurse well-being are critical to achieving WHO’s 2030 safety objectives. Empowering nurse leaders across all regions is essential for building safer, more resilient health systems. Full article

This study develops and psychometrically validates the Neuroplasticity Literacy in Working Life Scale (NLWLS), designed to evaluate employees’ engagement in enrichment activities and deliberate cognitive renewal practices. Based on a theoretical framework, neuroplasticity literacy is conceptualized through two behavioral dimensions: Enrichment Behaviors (EB) and Deliberate Cognitive Renewal (DCR). The scale was developed via a two-stage process involving expert evaluation, pilot testing, exploratory factor analysis, and confirmatory factor analysis using robust maximum likelihood estimation. Findings from two independent samples (n = 120; n = 164) consistently support the two-factor structure, demonstrating high internal consistency, strong convergent and discriminant validity, and satisfactory model fit indices. The NLWLS offers a methodologically rigorous instrument for measuring neuroplasticity-related behaviors at work, contributing to understanding employees’ cognitive renewal capacity, learning agility, and sustainable learning outcomes. These results support the integration of neuroscience-based behavioral indicators into organizational learning research and provide a theoretical–practical foundation for future studies. Full article

A course was created at a large private R1 university in the northeast U.S. to explore Diversity, Equity, Inclusion, and Accessibility (DEIA) in STEM in response to and to fulfill a university-wide DEIA requirement for undergraduates. To assess the effectiveness of the course, open-response pre- and post-tests were designed that measured students’ understanding of barriers to learning and participation across four underutilized groups in STEM: (1) women, (2) racial minorities, (3) people with disabilities, and (4) people raised in lower socioeconomic households. Written responses on the first and last day of class were analyzed for 69 unique students in three successive cohorts (Fall 2022, 2023, and 2024) and disaggregated by student-reported demographic data. A common codebook was developed that could be broadly applied to all four underutilized groups with overarching categories of individual/self; cultural/societal; and institutional/educational/career, with codes and subcodes specific to each category. Additionally, codes distinct to each underutilized group also emerged. As intended, students on average cited more total and unique barrier codes in the post-test than in the pre-test, confirming that the course had deepened their understanding of the multifaceted challenges and opportunities within educational systems and the broader culture that impact STEM inclusivity. When exploring STEM barriers for women, women reported more unique codes in the pre-test than men, but men showed higher gains from pre- to post-test. Similarly, White and Asian students showed greater gains than racial minority students when identifying STEM barriers for racial minorities. Students without disabilities reported a doubling in unique STEM barrier codes in the post-test. In these three groups, codes related to academic and workplace discrimination were commonly cited. Students who reported being from a low socioeconomic household were limited in this study, though these individuals included more unique codes in their pre-test responses on average. Students in this group commonly cited barriers related to access to opportunity. In general, we found that STEM students acquired significant understanding of barriers to STEM participation in the four underutilized groups of focus after completing a dedicated DEIA course. Additionally, learning gains were often greater in the majority (or privileged) demographic. Full article

The increased prevalence of mental health issues in the workplace affects employees’ well-being and organisational success, necessitating proactive interventions such as employee assistance programmes, stress management workshops, and tailored wellness initiatives. Artificial intelligence (AI) techniques are transforming mental health risk prediction using behavioural, environmental, and workplace data. However, the “black-box” nature of many AI models hinders trust, transparency, and adoption in sensitive domains such as mental health. This study used the Open Sourcing Mental Illness (OSMI) secondary dataset (2016–2023) and applied four ML classifiers, Random Forest (RF), xGBoost, Support Vector Machine (SVM), and AdaBoost, to predict workplace mental health outcomes. Explainable AI (XAI) techniques, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), were integrated to provide both global (SHAP) and instance-level (LIME) interpretability. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance. The results show that xGBoost and RF achieved the highest cross-validation accuracy (94%), with xGBoost performing best overall (accuracy = 91%, ROC AUC = 90%), followed by RF (accuracy = 91%). SHAP revealed that sought_treatment, past_mh_disorder, and current_mh_disorder had the most significant positive impact on predictions, while LIME provided case-level explanations to support individualised interpretation. These findings show the importance of explainable ML models in informing timely, targeted interventions, such as improving access to mental health resources, promoting stigma-free workplaces, and supporting treatment-seeking behaviour, while ensuring the ethical and transparent integration of AI into workplace mental health management. Full article

This article explores experts’ perspectives on the most important soft skills for entrepreneurial success in the Western Balkans (WB) and identifies effective educational and workplace practices to foster these skills. Using a qualitative Delphi study supported by a literature review, the research gathered and synthesized opinions from 20 experts representing Serbia, Albania, North Macedonia, and Bosnia and Herzegovina. Findings show that communication, adaptability, flexibility, teamwork, and critical thinking are essential for business success, while leadership, emotional intelligence, problem-solving, and teamwork are considered most vital for future entrepreneurs. Experts emphasized that group projects, specialized courses, and blended learning approaches are effective in educational settings, while workplace skill development benefits from training programs, mentoring, active communication, and openness to feedback. This study provides region-specific insights into skill-building strategies for young entrepreneurs, addressing a key research gap. By integrating expert consensus with evidence-based practices, the article offers a framework for educators, policymakers, institutions, and businesses to strengthen entrepreneurship education and workforce readiness across the WB region. Full article

Civil engineers play a central role in climate change adaptation, as they are responsible for designing and managing infrastructure that supports societal resilience. However, professional education has not kept pace with the growing demand for sustainability competencies. This paper proposes a pedagogical model for capacity building that equips engineers with the skills needed to integrate climate adaptation into their daily practice. Semi-structured interviews with stakeholders across Canada identified four pedagogical pillars of effective training: appreciation of climate risks, reflective practice, project-based learning, and design thinking. These were synthesized into the Model for Climate Change Adaptation through Appreciation and Engagement, which emphasizes both technical proficiency and transversal competencies such as collaboration, critical reflection, and ethical responsibility. By grounding climate knowledge in authentic, workplace-based contexts, the model bridges sustainability learning and engineering practice through a scalable training framework. It supports the advancement of Quality Education (SDG 4), Sustainable Cities and Communities (SDG 11) and Climate Action (SDG 13), while offering practical guidance to universities, professional associations, and policymakers seeking to accelerate climate adaptation in engineering education. Full article