Search Results (558)

Construction accident reports contain rich causal evidence; however, their unstructured narratives make systematic analysis difficult. Recent advances in large language models (LLMs) have created new opportunities to leverage such information at scale. This study develops an integrated LLM–BERT–network framework for analyzing construction accident causation. Based on 347 official construction accident investigation reports, a DeepSeek-based pipeline with human-in-the-loop quality control was used to extract causal keywords describing direct and indirect causes, yielding 2572 keywords. A BERT-based semantic normalization procedure then consolidated synonymous expressions, reducing 811 deduplicated keywords to 104 normalized terms (an 87.2% reduction in vocabulary size). A manual sample-based evaluation further supported the reliability of the LLM-based extraction and BERT-based normalization procedures. The normalized keywords were further organized into a hierarchical taxonomy and used to construct a directed keyword-association network linking indirect and direct causes for structured relational analysis. To strengthen methodological rigor, additional validation and analytical experiments were conducted, including manual sample-based evaluation of keyword extraction, sensitivity analysis of normalization settings, and examination of representative failure cases. The results support the reliability and robustness of the proposed framework. The analysis indicates that behavior-related factors and management deficiencies occupy structurally important positions in the directed network. Overall, the findings suggest that construction accidents arise from the interaction of human, managerial, environmental, material, and technical factors rather than isolated single causes. Effective prevention therefore requires system-oriented interventions that strengthen worker competence, supervision, training, accountability, and hazard identification. Full article

Emergency departments (EDs) are critical points of contact for treating and responding to the needs of Aboriginal and Torres Strait Islander women who have experienced traumatic brain injury (TBI) caused by violence. This study aimed to explore how care, support, and treatment can be improved for Aboriginal and Torres Strait Islander women who have experienced TBI caused by violence by drawing on the perspectives of ED staff in a regional hospital in Queensland (Australia). Using purposeful and snowballing sampling, 24 health professionals (including Indigenous hospital liaison officers and Aboriginal health workers and nursing, medical, and allied health staff) were recruited to participate in the study. Using reflexive thematic analysis, four key recommendations were identified: (1) development of a formalised pathway for head injury from family violence; (2) providing Aboriginal and Torres Strait Islander women with a timely acute-care-to-community pathway; (3) resourcing hospital- and community-based services for all Aboriginal and Torres Strait Islander women and their children; and (4) strengthening responses from health professionals to violence and head injury. The findings inform opportunities to strengthen ED and system-level responses to improve care and support for Aboriginal and Torres Strait Islander women who have experienced this injury. Full article

Stunting remains a major public health challenge in Indonesia, with a national prevalence of 21.6% in 2022. Rural regions such as Madura face heightened vulnerability due to cultural dietary taboos, gendered caregiving structures, intergenerational authority, and digital disparities that shape household nutrition decision-making. This formative qualitative study explores stakeholders’ perceptions to inform the conceptual development of gender-sensitive serious games for nutrition communication in rural Indonesia. Using an exploratory design, 42 informants, including mothers of children under five, brides-to-be, health cadres, midwives, religious and community leaders, and local digital actors, were recruited across rural Madura. Thematic analysis examined trust-based communication patterns, gender dynamics, perceptions of artificial intelligence (AI), and contextual conditions influencing digital health acceptance. Findings indicate that acceptance of gender-sensitive serious games depends on cultural alignment, institutional endorsement, perceived credibility, and usability in low-resource settings. Participants consistently positioned serious games and AI-supported features as complementary communication layers rather than replacements for health workers. Game-based tools were considered potentially relevant when designed to support intergenerational co-play, integrate local narratives and religious values, and function in low-connectivity environments. Rather than evaluating an implemented intervention, this study proposes a conceptual design framework grounded in feminist communication perspectives, serious games scholarship, and technology acceptance theory. The findings provide context-sensitive insights to guide future prototype development and pilot testing within hybrid, community-based nutrition communication systems. Full article

Background/Objectives: Healthcare workers (HCWs) have a crucial role in addressing vaccine hesitancy in ethnic minority populations as they are a trusted source of information. The aim of this systematic review is to synthesise and evaluate behaviour change techniques (BCTs) and strategies in interventions aimed at HCWs to promote vaccine uptake among ethnic minority populations. Methods: The literature was systematically searched in peer-reviewed databases and the grey literature. Studies were included if they reported interventions for respiratory and routinely recommended vaccine-preventable diseases which were delivered by HCWs to increase vaccine uptake in ethnic minority groups. Interventions were coded using the Behaviour Change Wheel (BCW) and BCT Taxonomy. Results: From 7250 records identified, 14 studies were included in the review. Vaccines targeted by interventions included influenza, pneumococcal disease, pertussis, tetanus, diphtheria, meningitis and hepatitis B. Seven BCW intervention types, six policy options and 22 BCTs were identified. Main intervention types used were persuasion, enablement and education. Effective interventions had multi-components and were tailored to specific populations. Staff training to improve vaccine recommendation and dialogue with patients, and prompts/cues were associated with positive effects, but there was no strong evidence to recommend one specific intervention strategy over another as effectiveness was linked to a multitude of BCTs and intervention types. Conclusions: Several strategies aimed at HCWs can be used and tailored to increase vaccine uptake among ethnic minority communities; however, this does not address all issues related to low vaccine uptake. While HCWs are necessary, without system-level enablement, they cannot fully address barriers to vaccine uptake. Full article

In collaboration with various stakeholders, including learners, parents, teachers, district administrators, and community organizations, school social workers (SSWs) can positively influence the school ethos, reduce risks and barriers to learning, and increase learners’ resilience. This collaboration is particularly crucial in low- and middle-income countries, where psychosocial well-being support structures are lacking, significantly affecting learners’ well-being. This exploratory qualitative study aimed to explore the nature of collaboration in the practice of school social work in South African public schools in Gauteng province. Semi-structured interviews were conducted with 17 SSWs who provided informed consent. Reflexive thematic analysis confirms that SSWs actively promote and participate in interdisciplinary and inter-organizational collaboration to support learners. These collaborations were crucial for addressing complex issues such as child protection, substance abuse, mental health, and poverty. However, the identified challenges include limited participation by experts, key stakeholders, and parents, as well as poor coordination between the social development and education departments. These challenges hinder SSWs’ ability to address learners’ diverse needs effectively. Therefore, the study argues for a shift toward more formalized, uniform partnerships and proposes operational strategies to strengthen collaboration and the practice of school social work within the broader school system. Full article

As the manufacturing industry continues to advance its digital transformation, intelligent sensing technology has become a key support for achieving precise, efficient and automated quality control. However, current production line monitoring systems predominantly rely on fixed and costly monitoring equipment and sensors, lacking flexible and interactive first-person perspective perception approaches centered on on-site operators. Meanwhile, factory process monitoring often depends solely on visual expression rather than balancing the capabilities of the simulation model and visual state detection, leading to delayed responses to abnormal systems and hindering the adjustment strategy feedback. To address these limitations, this study provides wearable sensing for key workers, enriching the state perception capabilities in industrial scenarios. Furthermore, to achieve dynamic model and real-time visual representation of production line operations, a multi-source information-enhanced Petri nets model is proposed in terms of engineering and user-friendliness. With the solid mathematical basics of the Petri nets and the enriched human–machine data from the product line, this method provides an intuitive, dynamic and accurate reflection of the production system’s real-time operational status, offering a scientific and reliable basis for operational decision-making. The proposed approach has been implemented in a real-world production system for reinforced concrete civil defense doors, and this engineering application can also be extended to many other scenarios. Full article

Background: Informal caregivers experience elevated psychological distress but face substantial challenges in accessing mental health support. Prior research has focused primarily on individual or interpersonal determinants, with less attention to how organizational, community, and policy contexts shape access and contribute to inequities in service availability. Methods: Fifty-one stakeholders, including 17 informal caregivers, 23 community health workers (CHWs), and 11 mental health professionals, completed a demographic survey and 60–90-min semi-structured interviews in English or Spanish. Data were analyzed using thematic analysis, combining deductive coding guided by the Socioecological Model with inductive identification of emergent themes. Results: Participants identified both barriers and supports influencing access to caregiver mental health support across socioecological levels. Individual-level barriers included limited mental health literacy, stigma, competing responsibilities, and language or technology challenges. Interpersonal barriers reflected family minimization of distress and limited encouragement for help-seeking. Organizational barriers involved program instability, restrictive eligibility criteria, long wait times, limited cultural responsiveness, and workplace constraints, while community-level influences included stigma and scarce affordable services. Policy-level barriers reflected immigration-related exclusions and the absence of caregiver-specific mental health coverage. Identified supports, such as CHW navigation and culturally responsive services, were often constrained by structural and organizational limitations. Conclusions: Caregivers face intersecting, multilevel barriers that constrain access to mental health support, while available supports are frequently insufficient to overcome structural constraints. Findings highlight the need for coordinated public health and systems-level strategies that address organizational and policy conditions shaping equitable access to caregiver mental health care. Full article

Generative Artificial Intelligence (AI) has transitioned from a research milestone to a general-purpose technology with wide-ranging implications for organizations, labor markets, and information systems. Thanks to improvements in deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformer-based language models, and reinforcement learning from human feedback (RLHF), generative AI can now create high-quality text, images, audio, code, and other types of content. This review synthesizes the core technical foundations and best practices for training, evaluation, and governance, with an emphasis on scalability and human oversight. The paper examines applications across customer service, marketing, software development, healthcare, finance, law, logistics, and the creative industries, and assesses the labor implications of generative AI using a sociotechnical lens. This study also develops a disruption index that integrates task exposure, adoption rates, time savings, and skill complementarity. The paper concludes with actionable recommendations for policymakers, organizations, and workers, emphasizing the importance of reskilling, algorithmic transparency, and inclusive innovation. Taken together, these contributions situate generative AI within broader debates about automation, augmentation, and the future of work. Full article

Preventing inpatient falls remains challenging for healthcare institutions globally, including in Singapore. Integrating technological innovations into fall prevention measures may optimize inpatient care and improve health outcomes. A multiphase study was conducted from 2019 to 2022, employing a human-centred design (HCD) approach to develop a technology-based inpatient fall prevention system (IFPS). The four phases include (1) pre-design observations and focus groups, (2) feature prioritization and wireframe development, (3) prototype testing and safety assessments, and (4) post-design staff training and feedback collection. The developed IFPS integrated artificial intelligence (AI) video analytics for bed-exit prediction with communication devices and autonomous commode delivery to facilitate ward communication and reduce staff workload. This paper describes the development process and user evaluation of the IFPS to assess its operational usability and safety. Potential users of the IFPS, such as ward nurses and patients, suggested features for the IFPS during the pre-design phase and thereafter evaluated the system through focus group discussions and/or feedback surveys. Pre-design focus group participants (n = 24) emphasized durability and user-friendliness requirements, informing system design. When evaluating the system, nurse users (n = 39) perceived the IFPS as effective in reducing falls (65%), enabling them to perform other duties (85%), and allowing them to remain with patients without searching for a commode (64%). Patient users (n = 21) found pre-recorded messages effective (91%), though communication clarity varied. Engaging healthcare workers in IFPS development offered valuable context-based insights, highlighting the importance of addressing technology acceptance factors early to promote adoption of fall prevention technologies in acute care settings. Full article

Oxidative alcohol metabolism in the liver relies on sequential enzymatic reactions involving alcohol dehydrogenase (ADH), cytochrome P450 2E1 (CYP2E1), and aldehyde dehydrogenase (ALDH) isozymes. However, the circadian regulation of these enzymes, their susceptibility to genetic, environmental, and metabolic disruption, and their functional implications toward alcohol-mediated tissue injury remain incompletely defined. To address this gap, we performed a comprehensive integrative analysis of the publicly available circadian transcriptome datasets spanning genetic clock disruption, acute sleep deprivation, chronic high-fat diet feeding, and occupational shift work to systematically characterize the temporal regulation and disruption vulnerability of the major alcohol-metabolizing enzymes. Mouse tissue-cycling analyses revealed pronounced gene- and tissue-specific diurnal regulation, with Adh1 oscillating primarily in adipose tissues; Cyp2e1 and mitochondrial Aldh2 cycling broadly across kidney, aorta, lung, adrenal gland, and liver; and cytosolic Aldh1b1 being uniformly arrhythmic. In the liver, Cyp2e1 and Aldh2 exhibited robust ~24 h oscillations that peaked during the light/resting phase, while Adh1 showed inconsistent rhythmicity and Aldh1b1 remained arrhythmic. Notably, Cyp2e1 and Aldh2 rhythms persisted in Bmal1 knockout and Clock mutant livers under light–dark conditions, despite complete loss of core clock gene oscillations, yet were abolished in constant darkness, revealing that systemic zeitgeber cues can mask the loss of intrinsic clock function to maintain apparent rhythmicity in these metabolic genes. Systematic cross-paradigm comparison established a novel gene-specific vulnerability hierarchy. Aldh2 was found to be most disrupted by environmental and metabolic perturbations, with acute sleep deprivation eliminating its rhythmicity and temporal expression pattern and a Western-style high-fat diet inducing pronounced phase delays and rhythm loss relative to low-fat diet controls. Both disruptions paralleled alterations in hepatocyte nuclear factor 4α (Hnf4a), newly implicating HNF4α as a potential mediator of ALDH2 circadian instability. In humans, ALDH2 and CYP2E1 exhibited conserved but phase-inverted circadian rhythms across multiple tissues relative to mice, and, importantly, night-shift workers showed markedly dampened and phase-shifted ALDH2 rhythms in peripheral blood mononuclear cells, providing the molecular link between occupational circadian misalignment and impaired acetaldehyde detoxification. Collectively, our detailed and innovative analytical approach reveals gene- and tissue-specific circadian regulation of alcohol-metabolizing enzymes, identifies ALDH2 as uniquely vulnerable to circadian misalignment, underscores the importance of circadian timing for optimal hepatic detoxification and resistance to tissue injury, and suggests that monitoring circadian rhythms could help tailor individualized advice on alcohol consumption for shift workers and populations with irregular sleep schedules, informing precision medicine approaches for alcohol-related disorders. Full article

The integration of electric micromobility into urban transportation systems can significantly reduce the energy consumption and emissions associated with short-distance travel. However, quantitative energy-based assessments remain limited, particularly in complex environments such as port–city interfaces. This paper presents a scenario-based energy assessment framework combining survey data and energy modelling. Empirical data were collected through a user survey (n = 138) targeting port workers and nearby residents, providing information on trip distances, travel frequency, modal choice, and willingness to shift from private car use. These data were combined with an energy modelling framework based on mode-specific energy intensity values expressed in kWh per passenger-kilometre. Three scenarios were analysed: a baseline scenario without intervention, a modal shift scenario supported by basic infrastructure measures, and an integrated scenario including transport management measures and local photovoltaic energy coupling. Results indicate that a moderate modal shift of 35% from private cars to electric micromobility for short-distance trips can generate aggregated annual energy savings of approximately 30 MWh and reduce CO₂ emissions by around 7 t per year across the analysed cases. According to the proposed energy model, electric micromobility achieves up to a 95% reduction in energy use per passenger-kilometre compared to private car travel. Furthermore, photovoltaic coupling could supply between 55% and 85% of the annual charging demand. The proposed framework is transparent and transferable, supporting energy-efficient and electrified future mobility planning. Full article

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article

One of the most important priorities of the most recent research work regarding the professional appraisal (PA) process is to understand different aspects of the social workers’ satisfaction with this particular type of professional evaluation. In this sense, this study addresses the imperative to comprehensively understand social workers’ satisfaction with PA, a pivotal yet sensitive human resource instrument within public administration. Drawing on a sociological survey of social workers in Romania’s North-West Development Region, the research empirically validated a multidimensional theoretical model of PA satisfaction (PAS) through rigorous exploratory and confirmatory factor analysis. The findings definitively establish that PAS is not a unidimensional construct, but rather a complex phenomenon underpinned by three distinct dimensions: (1) satisfaction with the most recent performance rating; (2) satisfaction with the appraisal system; and (3) satisfaction with the rater. This validated model significantly advances the conceptualization of satisfaction regarding PA, providing a precise diagnostic instrument for identifying systemic inefficiencies. Consequently, it offers a strategic framework for targeted organizational interventions and informs the development of more equitable and growth-oriented public policies. The study highlights that holistic measurement across these identified dimensions is crucial for cultivating employee motivation, reinforcing organizational justice, and fostering sustainable professional development within the public sector. Full article

Introduction: The global rise in antimicrobial resistance poses a growing threat to public health, particularly in low- and middle-income countries where diagnostic capacity and surveillance systems remain limited. In these settings, optimizing empiric antibiotic prescribing is critical, and syndromic antibiograms offer a promising approach to support evidence-based decision-making. This study examines anticipated barriers and facilitators to the adoption of syndromic antibiograms from the perspectives of pediatric clinicians and laboratory professionals at Maputo Central Hospital in Mozambique. Methods: Guided by the Dynamic Adaptation Process (DAP) framework, this qualitative study used semi-structured interviews with eighteen healthcare professionals to explore empiric antibiotic prescribing practices, perceptions of syndromic antibiograms, and system-level barriers and facilitators. Data were analyzed thematically using deductive codes derived from the DAP framework alongside inductive codes generated from participants’ narratives. Results: Barriers were identified at individual, organizational, and systems levels. Individual barriers included limited awareness, reliance on traditional practices, and resistance to change. Organizational barriers included weak leadership support, insufficient training, poor communication between clinicians and laboratory staff, suboptimal sample collection, heavy workloads, and staff shortages. Systems-level barriers comprised shortages of laboratory supplies and medicines, delays in laboratory results, and weak monitoring mechanisms. Facilitators included health worker motivation for evidence-based practice, organizational collaboration, peer and team support, and the presence of influential champions. Systems-level enablers included functional laboratory services, supportive institutional environments, alignment with clinical guidelines, and recognition of clinical utility. Conclusions: Successful implementation of syndromic antibiograms in LMIC will require addressing systemic and organizational barriers while fostering professional motivation, collaboration, and institutional support. Sustainable integration will depend on coordinated strategies—including resource strengthening, continuous training, supportive leadership, and structured monitoring—that collectively strengthen antimicrobial stewardship and inform health policy. Full article

Rockbursts are one of the most critical geomechanical hazards during the construction of deep tunnels under high in situ stress conditions, as they can compromise worker safety, damage infrastructure, and disrupt excavation continuity. Despite extensive research on rockburst mechanisms and mitigation, the causal analysis of individual events remains challenging due to the complex interaction between seismicity, geological conditions, stress redistribution, and operational factors. This study proposes a structured and multidisciplinary methodology for the causal analysis of rockbursts in deep high-stress tunnels. The methodology integrates seismicity analysis, geological and geotechnical characterization, operational assessment, field damage inspection, and hypothesis-driven interpretation to systematically reconstruct the sequence of processes leading to rockburst occurrence. The proposed approach is applied to a rockburst that occurred in 2020 in the Conveyor Belt tunnel (TC) of the Andes Norte Project, El Teniente Division, Codelco (Chile). The event reached a local magnitude of Mw = 1.7 and caused significant damage to tunnel support systems. Results indicate that the rockburst was associated with excavation- and blasting-induced stress redistribution, leading to the activation of a sub-horizontal rupture plane and subsequent damage propagation toward the excavated tunnel. The methodology provides a transparent and adaptable analytical framework for integrating multidisciplinary data into a coherent causal interpretation. Although demonstrated using a competent and brittle rock mass, the framework can be adapted to other deep tunneling projects under high-stress conditions by adjusting the governing parameters according to site-specific geological, geomechanical, and operational characteristics. The proposed approach supports improved understanding of rockburst mechanisms and informed decision-making for seismic risk management in deep underground excavations. Full article