Platform-mediated work (PMW) represents a highly unregulated and individualized segment of the labor market, with significant implications for psychosocial work environment and limited occupational health and safety (OHS) management efforts. The use of algorithmic management (AM) by digital platforms extensively directs and disciplines remote workers in PMW, and may exacerbate risks. This study employs the affordance concept initially introduced into safety science by Vicente and Rasmussen in 1992 and later applied in social media studies. Adopting a platform-sensitive approach, this study examines how digital mediation facilitates encounters between platform workers and customers across three types of PMW, and in turn affects harassment among platform workers. The analysis draws on 22 qualitative interviews with young platform workers supplemented by three workshops involving 13 stakeholder participants, informed by the Canadian Knowledge Transfer–Exchange approach. The findings identify three high-level affordances that significantly shape risks of harassment: (1) platforms’ ability to transcend physical space; (2) a digital blurring of private–professional boundaries; and (3) the amplification of asymmetric power relations among platform workers’ customers and platforms, relations that are gendered, classed, and racialized. The type and severity of harassment differ across the three types of platforms explored.

Background: Yoga has gained popularity worldwide and is generally considered a safe physical activity. However, injuries associated with yoga practice are increasingly reported, while data on cases requiring emergency care remain limited. Methods: A retrospective single-center study was conducted, analyzing cases of yoga-related injuries treated at a Swiss emergency department between 2013 and 2023. Medical records of 67 adult patients (aged ≥16 years) were reviewed for demographics, injury characteristics, management, and clinical outcomes. The study population consisted predominantly of females (76.1%), with a median age of 35 years. Results: Most injuries were musculoskeletal in nature and predominantly affected a single body region (95.5%). The most frequently involved areas were the head (29.9%), lower extremities (25.4%), and spine (19.4%). Soft tissue injuries, particularly muscle and tendon strains as well as contusions, were most common. Injury patterns differed across subgroups: older patients were more likely to sustain head injuries, whereas younger individuals more frequently presented with extremity injuries, including the rare cases of fractures and dislocations. Conservative treatment was sufficient in 94% of cases, although 20.9% of patients required hospitalization. Conclusion: Yoga-related injuries presenting to emergency care are generally minor and mainly involve soft tissues; however, injury patterns vary across demographic subgroups. Older adults appear more susceptible to balance-related and head injuries, while younger practitioners are more prone to acute extremity trauma. Recognizing these population-specific differences may support targeted prevention strategies and safer yoga practice.

Prismatic deflection loupes (PDLs) may offer ergonomic benefits over traditional through-the-lens (TTL) loupes and no loupe during ophthalmic microsurgery. Ten medical students performed microsuturing tasks under three conditions: PDL, TTL, and no loupes. Surface electromyography (EMG) captured bilateral upper trapezius activity, and a post-task 10-point Likert survey assessed comfort and related perceptions. Side-profile photos provided craniovertebral angles, which fed a trigometric model to estimate cervical spine loading (lbf) per condition. Relative to TTL, PDLs reduced upper trapezius activation by 17.2% (p = 0.009); relative to no loupe, PDL reductions were significant (p = 0.004). The TTL and no-loupe conditions did not differ significantly (p = 0.42). Comfort was highest for PDLs (7.8/10 on average); perceived strain was lowest with PDLs. CV angle and estimated cervical load were strongly inversely correlated (R2 = 0.94, p < 0.001). PDLs appear to reduce neck/shoulder muscle activity and cervical loading while enhancing comfort, supporting ergonomic value in ophthalmic surgery.

The present study examines the well-being of workers in an Italian manufacturing plant, focusing on work engagement, emotional exhaustion and work ability. These dimensions have received relatively little attention in manufacturing contexts. Utilising a person-centred approach, the objective is to identify distinct subjective well-being profiles among Italian manufacturing workers and to examine how work-related psychosocial characteristics differentiate these profiles. The research, which collected data from 340 workers (predominantly male at 62.1%) between July and September 2023, focused on work engagement, emotional exhaustion, and work ability—factors that have been previously understudied in manufacturing environments. Through cluster analysis, researchers were able to identify three worker profiles. The largest group, designated “Motivated & Healthy” (45.3%), exhibited the most favourable characteristics: strong work engagement, minimal emotional exhaustion, and adequate work ability. These workers reported experiencing reduced physical demands, greater autonomy in decision-making, and superior rewards compared to their colleagues. The second-largest group, “Motivated & Stressed” (32.5%), demonstrated a mixed profile. While maintaining average work engagement, these workers experienced high levels of emotional exhaustion and diminished work ability. The smallest group, termed “Disillusioned” (22.2%), consisted entirely of blue-collar workers and exhibited the most concerning pattern: low engagement, high exhaustion, and mediocre work ability. This group also reported the most challenging working conditions, including the highest physical and cognitive demands, least decision-making authority, and lowest rewards. The study corroborates earlier research findings by identifying significant relationships between work engagement and work ability (positive correlation) and emotional exhaustion (negative correlation). These results suggest that manufacturing facilities might benefit from tailoring their support strategies to address the specific needs of each worker profile, rather than applying one-size-fits-all solutions.

Performance-based contractor prequalification allows a construction client to evaluate contractors’ past performance on similar work, improving the likelihood of high-quality work being performed. Occupational safety and health criteria are sometimes included in such prequalification criteria. What metrics are predictive of safe future work? The objectives of this research were to (1) understand best practices in safety and health performance measurement, (2) conduct an initial desktop screening of all fifty US state Departments of Transportation (DOTs) for their publicly available safety and health prequalification criteria, and (3) propose a path forward for a state DOT research inquiry to raise the level of safety and health performance of all contractors by understanding their views of the feasibility and effectiveness of a list of possible leading indicators. Lagging indicators (injury rates, experience modification rates, regulatory citations) are traditional measures; they are shown to measure past performance but are not predictive of future safe work. There is no consensus on viable leading indicators in the discipline of safety science. Seventeen state DOTs require safety metrics of some kind as part of their prequalification process. Including safety and health criteria in the prequalification process provides an opportunity to help underperforming contractors, particularly small and historically underutilized businesses, raise the quality of their site safety and health performance.

Musculoskeletal disorders remain a persistent concern in automotive repair, yet empirical evidence on task-specific ergonomic risks in Middle Eastern contexts is limited. This study provides a detailed ergonomic risk profile of auto body specialists in Jeddah, Saudi Arabia, using a mixed-method approach that integrates the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and a validated Nordic Musculoskeletal Questionnaire. Twenty-five specialists across diverse tasks including installation, weighing, painting, cutting, and lifting were systematically evaluated to identify both postural and self-reported risk patterns. Results showed a high prevalence of discomfort in the lower back (64%), shoulders (52%), and wrists (48%). Ergonomic assessment revealed that the evaluated tasks were predominantly classified as moderate-to-high-risk, with RULA scores ranging from 6 to 7 and REBA scores ranging from 8 to 11. Beyond confirming the physical strain inherent to auto body work, the study highlights contextual factors such as prolonged static postures, limited use of mechanical aids, and constrained workshop layouts that exacerbate ergonomic risks. Importantly, the findings inform multi-level recommendations ranging from workshop practices to industry standards and policy considerations ensuring that interventions are both practical and scalable. By situating locally grounded results within the broader discourse on musculoskeletal risk prevention, the study offers region-specific evidence while providing globally relevant lessons for labor-intensive industries.

To identify and evaluate the failure issues in the livestock manure-to-biomethane supply chain, this study employs a Bayesian network approach with three inference analysis methods: diagnostic analysis, sensitivity analysis, and maximum causal chain inference. First, the main hazard categories affecting the failure of the supply chain are identified, establishing risk indicators for feedstock collection, pretreatment, anaerobic digestion, purification and upgrading, transportation, and biomethane end-use. Then, the half-interval method and possibility superiority comparison are used to calculate and rank the severity of related accidents, obtaining the severity ranking of secondary indicators as well as the severity ranking of work items and risk items. Finally, Bayesian forward inference is applied to investigate the failure probability of the supply chain, combined with backward inference to identify the risk factors most likely to cause supply chain failures and trace the formation of failure hazards. The Bayesian sensitivity analysis method is ultimately applied to determine the key hazards affecting supply chain failures and the correlations between accident hazards, followed by validation. The results show that the failure probability of the supply chain through causal inference is approximately 54.76%, indicating relatively high failure risk. The three factors with the highest posterior probabilities are mechanical stirring failure C3 (88.11%), corrosion-induced ammonia leakage poisoning D6, and equipment explosion caused by excessive pressure due to overheating during dehumidification heating D9, which are the hazards most likely to cause failures in the supply chain. Improper operations and the toxicity of related chemicals are key hazards leading to supply chain failures, with the correlation between accident hazards presented as a hazard chain by integrating severity and accident probability, and the key risk points in the supply chain are identified.

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.

Road traffic accidents are a major global public health concern, ranking among the top three causes of death worldwide and constituting the leading cause of death among individuals aged 15–29. Monitoring traffic safety status and trends is a vital element of effective road safety management. This study investigates road traffic casualties involving young car drivers (aged 18–24) in the Republic of Serbia from 1997 to 2024, analyzing historical patterns and introducing a predictive model for casualty outcomes. The analytical framework employs machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, to estimate the number of casualties (FSI = Fatal + Serious Injuries) based on various contributing factors. Accurate prediction of accident outcomes is essential for designing targeted road safety measures and reducing casualty numbers.

People with Autism Spectrum Disorder (ASD) face significant barriers to accessing and maintaining employment, many of which stem from work environments that fail to accommodate their neurological diversity. This article aims to analyze the occupational risks faced by autistic individuals in the workplace. A total of 39 scientific studies were reviewed, and the results identified nine predominant thematic categories of occupational risks. Particularly prominent were deficient communication, lack of structured support, cognitive overload, and difficulties coping with change. The reported situations were examined in detail, with attention paid to their specific contexts. A clear predominance of psychosocial risks over ergonomic ones was observed. The review also highlights several underexplored yet equally relevant risk factors, such as discontinuity in supported employment programs, difficulties in requesting reasonable accommodations, discrimination, a lack of professional recognition, and the negative effects of digital or remote environments, such as isolation. This study underscores the importance of recognizing unsafe conditions arising from the lack of neurodiversity-informed adjustments as a necessary step toward implementing organizational and social adaptations in the workplace.