Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

This study investigates the challenges of returning to sport after a COVID-19 shutdown of sport from the perspective of community sport clubs (CSCs). We explore the relationship between the challen...

Despite the increasing prevalence of artificial intelligence (AI) ethics frameworks, the practical application of these frameworks in industrial settings remains limited. This limitation is further augmented in energy systems by the complexity of systems composition and systems operation for energy generation, distribution, and supply. The primary reason for this limitation is the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems. For instance, trust is featured prominently in ethics frameworks but pertains to limited relevance for the robust operation of a smart grid. In this paper, we propose a lifecycle approach for AI ethics that aims to address this gap. The proposed approach consists of four phases: design, development, operation, and evaluation. All four phases are supported by a central AI ethics repository that gathers and integrates the primary and secondary dimensions of ethical practice, including reliability, safety, and trustworthiness, from design through to evaluation. This lifecycle approach is closely aligned with the operational lifecycle of energy systems, from design and production through to use, maintenance, repair, and overhaul, followed by shutdown, recycling, and replacement. Across these lifecycle stages, an energy system engages with numerous human stakeholders, directly with designers, engineers, users, trainers, operators, and maintenance technicians, as well as indirectly with managers, owners, policymakers, and community groups. This lifecycle approach is empirically evaluated in the complex energy system of a multi-campus tertiary education institution where the alignment between ethics and technical performance, as well as the human-centric application of AI, are demonstrated.

To identify the challenges adult community sport participants anticipate when returning to sport in Victoria, Australia, post a COVID‐19 shutdown. Using online concept mapping, participants brainstormed challenges to returning to community sport, sorted them into groups and rated them for impact and ability/capacity to overcome. Analysis included multidimensional scaling and hierarchical cluster analysis. Forty‐five community sport participants representing 24 sports identified 69 unique challenges to returning to sport. Eight clusters/questions participants need answered emerged from the sorting data (mean cluster impact and ability/capacity rating out of 5): Will we have enough participants? (3.32, 2.89); How do we stay safe? (3.31, 3.35); How will our sport change? (3.17, 2.85); How can we stay together? (3.15, 3.01); Will I be physically ready? (3.15, 3.05); What about the money? (2.86, 2.53); What about me? (2.65, 3.13); and What about the facilities? (2.49, 2.45). Participants perceived paradoxical challenges to returning to sport after COVID‐19 shutdown, which revolved around staying safe, staying connected and accessing meaningful sport activities. Sport organisations and public health practitioners should address the participant‐centred challenges identified in this study to maximise the public health benefits of participants returning to community sport.