Safety

Modern police work requires a high degree of versatility, shifting between sedentary tasks and intense physical demands. While bicycle patrols are recognized as a tool for enhancing community policing, few empirical studies have examined the specific nature and frequency of the tasks performed by bicycle patrol officers. This study aims to compare the professional tasks of bicycle and car patrol officers in the city of Saguenay, Québec, over a three-year period. A retrospective analysis of 539 computer-aided dispatch (PCAD) entries was conducted for eight male officers (six on bicycles, two in police cars) during the summer months of 2021 to 2023. We analyzed task frequency, duration, priority, and risk level using descriptive statistics. Results showed that while both patrol types performed similar core tasks, such as citizen assistance, enforcement of municipal regulations, and responses to suspicious individuals, bicycle patrols were associated with significantly longer total PCAD-recorded intervention times (49 ± 47 min vs. 33 ± 29 min). Moreover, the distribution of call types suggests a slightly higher proportion of interventions occurring in public spaces or involving direct citizen contact, although this does not constitute a measure of increased proximity. No significant differences were observed in terms of priority or risk. Because the PCAD system does not systematically record on-scene time, the longer durations observed for bicycle patrols cannot be interpreted as qualitative advantages. Instead, the study reveals operational similarities alongside noteworthy differences between patrol types. As one of the first Canadian CAD-based analyses of bicycle patrol tasks, this research underscores the need for future studies capable of isolating on-scene time and examining the qualitative dimensions of police–citizen interactions.

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.

To address the complex and uncertain causes of safety accidents in chemical enterprises, this study applied text mining techniques to systematically extract 29 causative factors from 422 accident reports. These factors were classified into five categories: personnel issues, resource management deficiencies, adverse organizational atmosphere, organizational process flaws, and inadequate supervision. Based on the extracted factors, a complex network model of accident causation was constructed. Using degree centrality, betweenness centrality, and eigenvector centrality, seven core causative factors were identified, along with multiple peripheral factors closely linked to them. Bayesian network-based sensitivity analysis further revealed the factors that exert the greatest influence on accident occurrence, and subsequent path analysis uncovered several critical accident propagation paths. The findings reveal core causative factors and critical propagation paths, which may inform the prioritization of risk control measures under conditions of limited resources.