Effectiveness of rational emotive occupational health coaching in reducing burnout symptoms among teachers of children with autism.

BACKGROUND/OBJECTIVES: The negative impacts of Job-related burnout on job performance have been widely documented in the literature. Burnout accounts for both physical and mental health outcomes that increase work turnover in teachers, especially those who teach special needs children, like those with Autism Spectrum Disorders (ASD). The current study assessed the effectiveness of Rational Emotive Occupational Health Coaching (REOHC) in minimizing job burnout amongst autistic children teachers in Anambra state, Nigeria. METHOD: The study used a group-randomized waitlist control trial design. teachers who teach ASD children in private and public special and inclusive schools participated in the study. All participants were randomly allocated to REOHC and waitlist group (WLG). REOHC group were exposed to a single session 120 min REOHC programme every week for 12 weeks. Data were collected using Maslach Burnout Inventory for Educators (MBI-ES), at baseline; post-intervention as well as follow-up evaluations 1 and 2 evaluations. All the data gathered for the study were analysed using mean, Standard Deviation (SD), t-test statistics, repeated measures ANOVA, and charts. RESULTS: Results indicated a significant decrease in teachers' burnout, following REOHC intervention, which was sustained through follow-ups 1 and 2. CONCLUSION: In conclusion, we stated that REOHC is valuable in treating burnout symptoms in teachers of children with ASDs.

Super-forecasting the 'technological singularity' risks from artificial intelligence.

This article investigates cybersecurity (and risk) in the context of 'technological singularity' from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently. Supplementary Information: The online version contains supplementary material available at 10.1007/s12530-022-09431-7.

Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X.

Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

The ethics of shared Covid-19 risks: an epistemological framework for ethical health technology assessment of risk in vaccine supply chain infrastructures.

This article addresses the topic of shared responsibilities in supply chains, with a specific focus on the application of the Internet of Things (IoT) in e-health environments, and Industry 4.0 issues-concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. In this article, we critically review methodologies and guidelines that have been proposed to approach these ethical aspects in digital supply chain settings. The emerging framework presents new findings on how digital technologies affect vaccine shared supply chain systems. Through epistemological analysis, the article derives new insights for transparency and accountability of supply chain cyber risk from Internet of Things systems. This research devises a framework for ethical awareness, assessment, transparency and accountability of the emerging cyber risk from integrating IoT technologies on shared Covid-19 healthcare supply chain infrastructure.

Artificial intelligence in cyber physical systems.

This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc.). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge.

The Internet of Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state of the art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.