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.

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.

Train driving is considered a high‐risk occupation for stress and work‐related mental health disorders. Sources of stress exposure during routine operations can impact a drivers' capacity for safe train operation. From a systems thinking perspective, safety is a responsibility shared by stakeholders across the rail transport system, with risk management relying upon those at the higher levels understanding the system state and the extent to which existing controls are achieving safe system functioning. In relation to driver stress, this requires that rail system stakeholders understand both the stressful events that train drivers are exposed to, and the impacts of this stress on driver performance. This research applied Rasmussen's Risk Management Framework to investigate and compare rail safety stakeholder perceptions on the impacts of acute stress on train driver performance. 119 rail stakeholders (drivers, guards, management, and regulators) were presented with three stressful event scenarios and asked to describe and rate impacts on driver performance. Qualitative analysis provided evidence of alignment of perceptions amongst drivers and other stakeholders, with themes indicating some agreement across rail system levels (e.g., across drivers, guards, supervisors, and management). Results revealed the most frequently reported stress impact related to cognition. Practical implications and future research directions to prevent and manage stressful event exposure are discussed.

Globally, identity theft incidents are increasing. When responding to identity theft incidents, various actors (i.e., individuals/organisations/agencies) interact within a complex response system. However, current literature has not addressed this complexity by using systems analysis methods to understand and potentially enhance the response to identity theft. The aim of this study was to use a systems analysis approach to identify vulnerabilities, flaws, and recommendations for improving the response system. To achieve this, two phases (Work Domain Analysis and Social Organisation and Cooperation Analysis) of the Cognitive Work Analysis framework were applied. The analysis demonstrates that the identity theft response system is complex and has a range of vulnerabilities and flaws, amongst these is the limited support for victim recovery. It is recommended that the system be re‐designed to focus more on restorative support for victims and to ensure that all aspects of victim outcomes are measured, rather than focusing solely on negative financial impacts.

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.