Gemma Read

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.

Substantial evidence suggests that university students experience poor mental health. However, the majority of previous studies focus on undergraduate student populations and common mental disorders (e.g. depression and anxiety). Less is known about the mental health of PhD students, specifically, their experiences of suicidality. The present study aimed to explore the experiences of suicidality among PhD students in Australia. PhD students across Australian universities were invited to complete an online survey. A sample of 299 participants completed the suicidality measure administered (Suicide Behavior Questionnaire-Revised), and results indicate that approximately 37% of participants were at risk of suicide. A subsample of 79 participants (58% at risk of suicide) provided textual response to elaborate on their suicidality experiences, and qualitative content analysis indicated that there were experiences of suicidality that were unrelated to the PhD but also experiences of suicidality that were related to the PhD. Several risk factors for suicidality were mentioned by participants, including PhD pressures and stress, and anxiety about the future. Protective factors that were mentioned included the PhD providing a sense of purpose and the presence of social and psychological support. These results call for action in higher education systems, with the findings suggesting opportunities for universities to implement strategies that better safeguard the mental health of PhD students.

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.

Young driver safety remains a road safety priority due to the over-representation of this cohort in crashes, fatalities and injuries. Speeding is recognised as a key contributor to traffic crashes, particularly within this population. Traffic safety culture is a growing area of research that considers the shared attitudes, values and beliefs of communities relating to road safety behaviours. However, the traffic safety culture of young drivers is under-researched and has not been conceptualised for speeding behaviour specifically. The current study applied an exploratory approach paired with systems thinking to discern the traffic safety culture of young drivers in Queensland, and in sub-groups of young drivers (general population females, general population males and car enthusiasts). Further, factors across the road transport system were investigated for their impact on traffic safety culture and speeding behaviour. A sample of 27 young drivers from Queensland participated in semi-structured interviews. Thematic analysis revealed key factors of a general traffic safety culture that exist across the young driver groups. Further, the findings showed that factors across the whole road transport system impact young drivers’ traffic safety culture to varying degrees. The findings provide insight into how the system could be adapted to encourage safer behaviour in young drivers. It is suggested that a holistic approach spanning the road transport system be applied to integrate strategies which promote a positive traffic safety culture around speeding within the young driver cohort.

Society faces a growing set of risks from advanced emerging technologies. While there has been discussion on some of these risks, a comprehensive overview does not exist, and it is not clear what methods are suited to identify future risks. This scoping review aimed to synthesise current knowledge regarding the risks associated with emerging technologies. The findings show that a diverse set of technologies and risks have been considered, with ten risk themes identified: risks to human health and wellbeing, sub-standard technology risks, legal and ethical risks, privacy and security risks, socioeconomic impacts, ecological and environmental risks, malicious use risks, geopolitical risks, technological unemployment risks, and existential threats. It is concluded that there is a need to expand the focus of prospective risk assessments to consider the organisational, sociotechnical and societal systems in which emerging technologies will be deployed. The development of a future technology risks classification scheme is also recommended.

The introduction of advanced digital technologies continues to increase system complexity and introduce risks, which must be proactively identified and managed to support system resilience. Brain-computer interfaces (BCIs) are one such technology; however, the risks arising from broad societal use of the technology have yet to be identified and controlled. This study applied a structured systems thinking-based risk assessment method to prospectively identify risks and risk controls for a hypothetical future BCI system lifecycle. The application of the Networked Hazard Analysis and Risk Management System (Net-HARMS) method identified over 800 risks throughout the BCI system lifecycle, from BCI development and regulation through to BCI use, maintenance, and decommissioning. High-criticality risk themes include the implantation and degradation of unsafe BCIs, unsolicited brain stimulation, incorrect signals being sent to safety-critical technologies, and insufficiently supported BCI users. Over 600 risk controls were identified that could be implemented to support system safety and performance resilience. Overall, many highly-impactful BCI system safety and performance risks may arise throughout the BCI system lifecycle and will require collaborative efforts from a wide range of BCI stakeholders to adequately control. Whilst some of the identified controls are practical, work is required to develop a more systematic set of controls to best support the design of a resilient sociotechnical BCI system.

Identifying design requirements for new technologies is vitally important for successful uptake, yet few structured approaches exist to guide the process. Given increasing concerns regarding gig economy worker road safety, this study involved the use of Cognitive Work Analysis (CWA), alongside a participatory design process, to identify requirements for a novel, app-based incident reporting tool for gig economy workers. Study 1 involved a stakeholder workshop to refine a Work Domain Analysis model and define stakeholder requirements for the tool. Study 2 involved focus groups to identify barriers and facilitators to using the tool and a survey to inform the development of a Contextual Activity Template, in addition to identifying further barriers and facilitators. Design requirements for the tool were identified through synthesising the CWA outputs and themes identified from stakeholder and end user input.

Dysfunctional behavior in active military personnel is a complex and challenging issue for military forces worldwide. Effective management of this issue requires a comprehensive understanding of the factors that contribute to dysfunctional behavior in military populations. The current study presents an umbrella review that synthesized and analyzed the existing literature on contributory factors to dysfunctional behavior in active military personnel using a systems thinking-based framework. Eleven systematic reviews were identified as eligible for inclusion in the umbrella review. The synthesis identified 14 contributory factors to the following types of dysfunctional behavior: suicidal behavior, substance misuse, domestic violence perpetration, and destructive leadership. The analysis indicated that existing literature focuses on contributory factors relating to the military personnel themselves and not influences in the broader military system or wider society. Additionally, few studies have sought to understand how factors interact to create dysfunctional behavior. Future research would benefit from the use of systems thinking-based frameworks and methods to investigate the factors, across the broader military system and society, that contribute to dysfunctional behavior in active military personnel.