Gemma Read

Technology-facilitated intimate partner violence (TFIPV) is increasingly featured in cases of intimate partner violence and intimate partner homicide. There is emerging recognition of the use of technology to abuse and control intimate partners in domestic abuse research and the inherent complexity of TFIPV is evident. As such, a better understanding of the factors that influence TFIPV is necessary. This scoping review aimed to synthesise extant literature on factors known to influence TFIPV. The review included 84 peer-reviewed primary sources that focused on adult intimate relationships (at least 18 years of age), had full-text versions in English, and included individual, situational, environmental, technological, and systemic factors found to increase the likelihood of TFIPV. The influencing factors were extracted and mapped onto a systems-thinking-based framework, Rasmussen's Risk Management Framework. This synthesis identified 28 higher-order themes across the TFIPV system hierarchy. Most of the factors within these themes related to lower levels of the system (e.g. perpetrators, victim-survivors, social environment, device and software capabilities), with limited consideration of broader systemic factors that influence TFIPV. Of the broader systemic factors that were identified, most related to inadequate responses to TFIPV by government, judicial system, technology industry regulators, and police. Future research should consider the use of systems-thinking-based frameworks and methods in understanding and addressing this complex and pervasive problem from a whole-of-system perspective.

Remotely Piloted Aircraft Systems (RPAS), also known as drones, are a rapidly emerging technology that is being quickly adopted in many civil and military applications. Although there are several benefits, a number of safety risks also exist. It is essential to take a proactive approach in designing broader sociotechnical safety systems that support the safe and optimal use of this technology. We used the Systems Theoretic Accident Model and Processes (STAMP) method to develop an optimal control structure needed to ensure safe and effective RPAS operations.

Technology-facilitated abuse (TFA) is prevalent in cases of intimate partner violence. This scoping review applied a systems ergonomics model, Rasmussen’s Risk Management Framework, as a novel approach to map the known factors that influence TFA in the context of intimate partner violence. The findings showed that the literature has focused on factors related to actors directly involved in TFA (e.g., perpetrators and victim-survivors) and that inadequate systemic responses influence the persistence of TFA. The utility of applying systems ergonomics to understand and address complex and pervasive social problems was demonstrated.

Road transport systems worldwide continue to create unacceptable levels of trauma. Currently, there are no widely adopted Human Factors Integration (HFI) guidelines specifically developed for road transport systems. This project outlines the development of a guideline to support the application of Ergonomics and Human Factors (EHF) knowledge, principles, and methods in the design and operation of road transport systems. Existing guidelines were reviewed, and a model for an optimal HFI guideline was developed using Work Domain Analysis with stakeholder input.

Remotely Piloted Aircraft Systems (RPAS), also known as drones, are a rapidly emerging technology being quickly adopted in civil and military applications. Although there are many potential benefits, several safety challenges also exist. This requires a proactive approach to design the broader sociotechnical safety system required to support the safe and optimal use of this technology. This study provides a novel application of the Systems Theoretic Accident Model and Processes (STAMP) safety control structure model to develop a proposed control structure for safe and effective RPAS operations. Following subject expert review and validation, the proposed STAMP control structure served as the basis for evaluating the current RPAS safety system’s maturity and identifying gaps between the proposed and current structures. The findings revealed several gaps at all levels of the safety control structure model. Based on a review of these gaps, a series of required control actions and feedback mechanisms is presented. A national RPAS strategy was identified as a key control action mechanism that could support safe and effective RPAS operations. Overall, the study represents a novel application of STAMP to a rapidly emerging and disruptive technology; the implications for future applications in other contexts are discussed.

Suicide is a leading cause of premature mortality worldwide, making suicide prevention a global public health priority. As more Artificial Intelligence (AI)-based suicide prevention interventions are being developed and implemented, it is important to study acceptance of these AI systems. The present study aimed to investigate the factors that predict acceptance of current and future AI systems for suicide prevention and the perceived risks and benefits of these AI systems. Individuals from the Australian public were invited to participate in an online survey, which included six hypothetical scenarios of current AI systems (Artificial Narrow Intelligence [ANI]) and future advanced AI systems (Artificial General Intelligence [AGI]) for suicide prevention. Participants evaluated these scenarios on five technology acceptance factors (performance expectancy, effort expectancy, social influence, facilitating conditions, trust) and elaborated on its perceived risks and benefits. Performance expectancy, social influence, and trust predicted acceptance of ANI systems, but only trust predicted acceptance of AGI systems. Overall, the level of acceptance was higher for ANI systems than for AGI systems. Several perceived risks (e.g., risks to mental healthcare, distrust in AI, threats to humanity) and perceived benefits (e.g., benefits to mental healthcare, trust in AI, human help-seeking) were identified. AI systems represent potential avenues for effective suicide prevention. However, to ensure acceptance, AI systems for suicide prevention must be developed in a way that is safe, reliable, and trustworthy.

Teams play a critical role in society and represent a key area for Human Factors and Ergonomics. Salas et al.'s Big Five model is widely cited; however, the increasing use of Human-Autonomy Teams (HATs) has fuelled debate over its continued relevance. It is important to reflect on how the Big five model has been applied, in what contexts, and whether applications to contemporary teams are emerging. This article presents the findings from a scoping review undertaken to identify and synthesise the peer reviewed literature describing applications of the Big Five model. Articles were deemed eligible for inclusion if they were published in the peer reviewed literature and described an application of the Big Five model to study teamwork. 38 articles were included in the review and no applications of the Big Five model to study HATs were identified. Over half of the studies were undertaken in healthcare and a range of assessment methods have been used (e.g., questionnaires, surveys, interviews, observer-rating scales, communication transcript analysis). Just under a third of included studies evaluated all model components (i.e., the five processes and three coordinating mechanisms) and few considered the relationships between model components or between model components and team effectiveness. Research is required to explore the validity of the Big Five model for HATs, to gather evidence for the relationship between model components and team effectiveness, and to develop more precise Big five-based measures.Teams play a critical role in society and represent a key area for Human Factors and Ergonomics. Salas et al.'s Big Five model is widely cited; however, the increasing use of Human-Autonomy Teams (HATs) has fuelled debate over its continued relevance. It is important to reflect on how the Big five model has been applied, in what contexts, and whether applications to contemporary teams are emerging. This article presents the findings from a scoping review undertaken to identify and synthesise the peer reviewed literature describing applications of the Big Five model. Articles were deemed eligible for inclusion if they were published in the peer reviewed literature and described an application of the Big Five model to study teamwork. 38 articles were included in the review and no applications of the Big Five model to study HATs were identified. Over half of the studies were undertaken in healthcare and a range of assessment methods have been used (e.g., questionnaires, surveys, interviews, observer-rating scales, communication transcript analysis). Just under a third of included studies evaluated all model components (i.e., the five processes and three coordinating mechanisms) and few considered the relationships between model components or between model components and team effectiveness. Research is required to explore the validity of the Big Five model for HATs, to gather evidence for the relationship between model components and team effectiveness, and to develop more precise Big five-based measures.

Signal Passed at Danger (SPAD) incidents, though infrequent, remain a serious threat to rail safety. While often investigated as discrete operational events, SPADs are symptomatic of deeper system conditions. Investigations tend to follow linear logics of human error and focus corrective actions on individual behaviour—especially that of the train driver. However, such approaches may obscure rather than reveal meaningful system-wide learning. This meta-analysis used the AcciMap technique to analyse and aggregate findings from SPAD investigation reports (N = 49), offering a unique holistic perspective. A total of 859 contributory factors were identified (∼17 per SPAD), with most (∼62 %) reported to occur at the level of physical processes and actor activities. The largest aggregate contributory factor being attributed to SPADs was driver cognitive (dis)engagement (63 factors). Thematic aggregation revealed four themes: (1) A signal lost in the noise, relating to information overload in the rail environment and inevitable separation of human attention from signals; (2) Lost in translation, highlighting interchangeable and indistinct use of labels and concepts employed during investigation; (3) Lost in the brake down, reflecting a dominant narrative of driver (in)action and wrong-doing; and (4) Lost in the loop, pointing to surface-level corrective actions that repeated what was expected rather than redesign ineffective controls. Findings are discussed by reflecting on the question: “what might SPADs be trying to teach us?” By shifting the analytical lens from individual to system, this study challenges dominant narratives of driver blame and considers whether current investigations genuinely reflect systems thinking or merely perpetuate an illusion of learning.

Level crossings, where road and rail intersect, pose significant safety risks to train drivers, road users, pedestrians, and cyclists (Edquist et al., 2009; Read et al., 2021). From 2014 to 2022, 322 collisions at Australian level crossings resulted in 39 fatalities and 49 serious injuries (National Level Crossing Safety Committee, 2023; Read et al., 2021). Beyond the significant human and social costs, collisions at level crossings impose a considerable economic burden, with the annual financial cost estimated to exceed AUD $116 million in 2010 (Tooth & Balmford, 2010).
Human Factors issues play a critical role in level crossing collisions (Beanland et al., 2018; Salmon, Read, et al., 2013). Collisions can occur because road users misjudge the speed or distance of an approaching train, rely on past experience to anticipate train movements, or fail to perceive warnings or even trains due to low expectancy (Caird, 2002; Office of the National Rail Safety Regulator, 2024; Salmon, Lenné, et al., 2013; Salmon, 2013; Young, 2015). Studies show that both active crossings (e.g., boom gates, flashing lights) and passive crossings (e.g., stop signs, no warning systems) present risks, with user behaviour shaped by the physical and informational environment (Lenné et al., 2011; Read et al., 2021). Despite substantial research, Human Factors knowledge has not been systematically integrated into level crossing design and evaluation, particularly in rural and regional areas where passive controls dominate (Bearman, 2013).
Given the costs associated with installing traditional active controls, there is growing interest in innovative, low-cost safety solutions (Silla, 2019; Wullems, 2011). However, developers, engineers, and project managers often lack practical guidance on applying Human Factors principles during the design, implementation, and operation of these solutions. This gap can reduce the effectiveness of interventions. This project addresses the gap through the development of a publicly accessible, evidence-based toolkit to help embed Human Factors principles into the design, implementation, and operation of innovative level crossing safety solutions.