Development of Rasmussen's risk management framework for analysing multi-level sociotechnical influences in the design of envisioned work systems

Identification and analysis of hoisting safety risk factors for IBS construction based on the AcciMap and cases study

Hoisting is an essential aspect of Industrial Building System (IBS) construction. Although research on hoisting safety in China has made strides to focus on "worker," "data," "task," "site," and "accident," there still needs to be more approaches based on multi-dimensional social system thinking. Therefore, the paper aims to fill this gap. We investigated 105 hoisting accidents in China and found that hoisting accidents occurred most frequently in China's southeast coastal region; truck-mounted cranes and tower cranes were the most common types of machinery involved in accidents; hoisting load off, capsizing of crane machinery, and workers falling from height are the three most common accident types; the average impact of a single hoisting accident is approximately RMB 2.43 million direct economic loss, 1.543 deaths and 0.829 injured. This study used three algorithms (Rindge regression, Lasson regression, and partial least squares regression) to explore the impact of deaths and injuries on direct economic losses. By combining Rasmussen's risk framework with the characteristics of hoisting construction, six risk domains and thirty-six safety risk factors were identified. Finally, we used AcciMap technology to construct a qualitative IBS hoisting management model, which exhaustively presents the systematic levels and propagation paths of the influencing factors by the PDCA method. The research helps academics explore strategies to improve the safety of hoisting construction in IBS. Moreover, the study outcomes can inform the policy-making process towards promoting healthy and sustainable construction development.

Reimagining the joint task force core competency framework for rural and frontier clinical research professionals conducting hybrid and decentralized trials

Introduction: Clinical research professionals (i.e., clinical research assistants, clinical research nurses, clinical research coordinators, etc.), as outlined by the Joint Task Force (JTF) Core Competency Framework, are highly trained to support the breadth of clinical trial operations and manage participant care. Clinical research professionals are uniquely equipped with a scope of practice that permits product administration, participant assessments, and data management. As clinical trials grow in complexity and their management expands beyond traditional, site-based operations models to decentralized and/or hybrid models, the need becomes great to ensure adequate staffing. However, rural hospitals frequently lack the research staff or patient recruiters that would allow them to support decentralized clinical trials across a sizeable rural geographic demographic. Methods: This paper examines the contributory factors of the clinical research professional workforce contraction and response efforts at professional and organizational levels within a large, Magnet-designated healthcare system in the rural northwestern United States. Perspectives are shared on adapting the Core Competency Framework to reflect the unique strengths and opportunities towards decentralized trials in rural regions of the United States and areas of priority for workforce cultivation and retention. A descriptive survey was used to gather initial data identifying the current research perspectives of healthcare workers working across a rural community. Participants were asked to complete questions about the JTF Competency domains and behavior-based questions. Analysis: Both competency and behavior-based questions were asked and related to roles. These were then cross-referenced using a Rasmussen Ladder system. Descriptive statistics were conducted for sample characteristics, self-reported competency domain questions, and behavior questions. Results and discussion: Survey findings suggest that although healthcare workers and clinical research teams interact, they are unlikely to ask their patients to participate in research. Based on the limited response rate, results suggest that better education throughout the rural community could benefit from decentralized research efforts. Increased use of technology was also highlighted as an area of interest.

Shaping the development and use of Artificial Intelligence: how human factors and ergonomics expertise can become more pertinent

New developments in Artificial Intelligence (AI) are extensively discussed in public media and scholarly publications. While in many academic disciplines debates on the challenges and opportunities of Artificial Intelligence (AI) and how to best address them have been launched, the human factors and ergonomics (HFE) community has been strangely quiet. I discuss three main areas in which HFE could and should significantly contribute to the socially and economically viable development and use of AI: decisions on automation versus augmentation of human work; alignment of control and accountability for AI outcomes; counteracting power imbalances among AI stakeholders. I then outline actions that the HFE community could undertake to improve their involvement in AI development and use, foremost translating ethical into design principles, strengthening the macro-turn in HFE, broadening the HFE design mindset, and taking advantage of new interdisciplinary research opportunities.Practitioner summary: HFE expertise could and should significantly contribute to the socially and economically viable development and use of AI. Translating ethical into design principles, opening up to broader multi-stakeholder perspectives, and engaging in interdisciplinary collaboration within a design science framework are discussed as measures to achieve that.

Designing human-AI systems for complex settings: ideas from distributed, joint, and self-organising perspectives of sociotechnical systems and cognitive work analysis

Real-world events like the COVID-19 pandemic and wildfires in Australia, Europe, and America remind us that the demands of complex operational settings are met by multiple, distributed teams interwoven with a large array of artefacts and networked technologies, including automation. Yet, current models of human-automation interaction, including those intended for human-machine teaming or collaboration, tend to be dyadic in nature, assuming individual humans interacting with individual machines. Given the opportunities and challenges of emerging artificial intelligence (AI) technologies, and the growing interest of many organisations in utilising these technologies in complex operations, we suggest turning to contemporary perspectives of sociotechnical systems for a way forward. We show how ideas of distributed cognition, joint cognitive systems, and self-organisation lead to specific concepts for designing human-AI systems, and propose that design frameworks informed by contemporary views of complex work performance are needed. We discuss cognitive work analysis as an example.