The paper describes the study design, research questions and methods of a large, international intervention project aimed at improving employee mental health and well-being in SMEs and public organisations. The study is innovative in multiple ways. First, it goes beyond the current debate on whether individual- or organisational-level interventions are most effective in improving employee health and well-being and tests the cumulative effects of multilevel interventions, that is, interventions addressing individual, group, leader and organisational levels. Second, it tailors its interventions to address the aftermaths of the Covid-19 pandemic and develop suitable multilevel interventions for dealing with new ways of working. Third, it uses realist evaluation to explore and identify the working ingredients of and the conditions required for each level of intervention, and their outcomes. Finally, an economic evaluation will assess both the cost-effectiveness analysis and the affordability of the interventions from the employer perspective. The study integrates the training transfer and the organisational process evaluation literature to develop toolkits helping end-users to promote mental health and well-being in the workplace.

Background: The COVID-19 pandemic has strained hospitals and healthcare workers engaged in combating the virus with limited knowledge and resources. Intensive care unit (ICU) nurses are among the healthcare workers most affected by the pandemic and are at risk for developing burnout syndrome. Objective: The present study aims to explore burnout symptoms prevalence among ICU nurses and to identify the individual, organizational, and contextual risk, and protective factors of burnout in ICU nurses during the COVID-19 pandemic. Methods: A scoping review was conducted by searching PubMed, Scopus, and Web of Science. Only papers with empirical data and referred to ICU nurses were included. A total of 350 initial results were yielded, and 40 full texts were screened. Twelve papers constituted the final sample in the analysis. Results: High levels of symptoms of burnout (emotional exhaustion, depersonalization, and reduced personal accomplishment) were registered among ICU nurses during the COVID-19 pandemic. Increased workload, lack of equipment, social stigma, and fear of contagion emerged as key risk factors. Social support from leaders and colleagues, professional recognition, use of personal protective tools, and witnessing patients’ successful recovery emerged as major protective factors. Conclusions: The results may inform the development of timely actions to counter burnout in ICU nurses during this COVID-19 pandemic and in a post-COVID-19 scenario.

Background: Interventions tackling COVID-19 impact on health care workers’ mental health would benefit from being informed by validated and integrated assessment frameworks. This study aimed to explore the fitness of integrating the Job Demands-Resources (JD-R) model and the Individual-Group-Leader-Organization (IGLO) framework to investigate the pandemic’s impact on health care workers’ mental health. Methods: Qualitative data were collected via 21 semi-structured interviews with senior and middle managers and four focus groups with employees (doctors, nurses, health care assistants) from three areas (Department of Emergency, Department of Medicine, Research Institute of Neuroscience) of a large health care institution facing the first wave of COVID-19. NVivo deductive content analysis of text data was performed. Findings: Several COVID-19-related job demands and resources were found at IGLO levels. Individual-level demands included emotional load, while resources included resilience and motivation. Group-level demands included social distancing, while resources included team support and cohesion. Leader-level demands included managers’ workload, while resources included leader support. Organizational-level demands included work reorganization, while resources included mental health initiatives. Conclusions/Application to Practice: Integrating JD-R and IGLO proved feasible, as job demands and resources could be categorized according to the individual, group, leader, and organization framework. The findings expand previous studies by filling the lack of knowledge on how job demands and resources might unfold at different workplace levels during a pandemic. Results provide unit-level evidence for designing and implementing multilevel interventions to manage health care workers’ mental health during COVID-19 and future pandemics. Our findings offer occupational health practitioners a suitable approach to perform workplace mental health assessment activities.

Aim/Purpose: This paper aims to investigate the recent developments in research and practice on the transformation of professional skills by artificial intelligence (AI) and to identify solutions to the challenges that arise. Background: The implementation of AI in various organisational sectors has the potential to automate tasks that are currently performed by humans or to reduce cognitive workload. While this can lead to increased productivity and efficiency, these rapid changes have significant implications for organisations and workers, as AI can also be perceived as leading to job losses. Successfully adapting to this transformation will lead companies and institutions to new working and organisational models, which requires implementing measures and strategies to upskill or reskill workers. Organisations, therefore, face considerable challenges such as guiding employees towards the change process, dealing with the cost of training, and ensuring fairness and inclusion posed by age, gender, and cultural diversity. Methodology: A narrative review has been conducted to analyse research and practice on the impact of AI on human skills in organisations. Contribution: This work contributes to the body of knowledge by examining recent trends in research and practice on how AI will transform professional skills and workplaces, highlighting the crucial role played by transversal skills and identifying strategies that can support organisations and guide workers toward the upskilling and reskilling challenges. Findings: This work found that introducing AI in organisations combines many organisational strategies simultaneously. First, it is critical to map the transversal skills needed by workers to mitigate the current skills gap within the workplace. Secondly, organisations can help workers identify the skills required for AI adoption, improve current skills, and develop new skills. In addition, the findings show that companies need to implement processes to support workers by providing ad hoc training and development opportunities to ensure that workers’ attitudes and mental models towards AI are open and ready for the changing labour market and its related challenges. Recommendation for Researchers: AI is a complex and multifaceted field that encompasses a wide range of disciplines, including computer science, mathematics, engineering, and behavioural and social sciences. Researchers should take a transdisciplinary approach to enable the integration of knowledge and perspectives from different fields that are essential to understanding the full range of implications and applications of AI. Future Research: Further research is needed to understand the impact of AI on human skills and the role of soft skills in the adoption of AI in organisations. Future studies should also consider the challenges presented by Industry 5.0, which is likely to involve the integration of new technologies and automation on an even greater scale.

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