Aim/Purpose: This paper examines the transformative impact of Artificial Intelligence (AI) on professional skills in organizations and explores strategies to address the resulting challenges. Background: The rapid integration of AI across various sectors is automating tasks and reducing cognitive workload, leading to increased productivity but also raising concerns about job displacement. Successfully adapting to this transformation requires organizations to implement new working models and develop strategies for upskilling and reskilling their workforce. Methodology: This review analyzes recent research and practice on AI's impact on human skills in organizations. We identify key trends in how AI is reshaping professional competencies and highlight the crucial role of transversal skills in this evolving landscape. The paper also discusses effective strategies to support organizations and guide workers through upskilling and reskilling processes. Contribution: The paper contributes to the existing body of knowledge by examining recent trends in AI's impact on professional skills and workplaces. It emphasizes the importance of transversal skills and identifies strategies to support organizations and workers in meeting upskilling and reskilling challenges. Our findings suggest that investing in workforce development is crucial for ensuring that the benefits of AI are equitably distributed among all stakeholders. Findings: Our findings indicate that organizations must employ a proactive approach to navigate the AI-driven transformation of the workplace. This approach involves mapping the transversal skills needed to address current skill gaps, helping workers identify and develop skills required for effective AI adoption, and implementing processes to support workers through targeted training and development opportunities. These strategies are essential for ensuring that workers' attitudes and mental models towards AI are adaptable and prepared for the changing labor market. Recommendation for Researchers: We emphasize the need for researchers to adopt a transdisciplinary approach when studying AI's impact on the workplace. Given AI's complexity and its far-reaching implications across various fields including computer science, mathematics, engineering, and behavioral and social sciences, integrating diverse perspectives is crucial for a holistic understanding of AI's applications and consequences. Future Research: Looking ahead, further research is needed to deepen our understanding of AI's impact on human skills, particularly the role of soft skills in AI adoption within organizations. Future studies should also address the challenges posed by Industry 5.0, which is expected to bring about even more extensive integration of new technologies and automation.

Bicycle–motorised vehicle (BMV) collisions account for the majority of the recorded bicyclists’ fatalities and serious injuries. This systematic review intends to examine the main factors contributing to BMV collisions. We performed a comprehensive literature review on Scopus, TRID, ProQuest, and Web of Science databases. Fifty-nine English-language peer-reviewed articles met the eligibility criteria and were included in the final analysis. The main factors contributing to BMV collisions identified were classified in accordance with a recently published conceptual framework for road safety. The majority of studies have identified factors related to road users’ behaviour (59.3%) and infrastructure characteristics (57.6%). A minority of studies identified variables related to exposure (40.7%) and vehicles (15.3%) as contributor factors to BMV collisions. A small but significant proportion of studies (20.3%) provided evidence that environmental factors may also play a role, although to a lesser extent, in determining BMV collisions. In addition to the three factors comprised in the applied conceptual framework for road safety, we identified environmental conditions as a category of factors contributing to BMV collisions.

Accident analysis and studies on traffic revealed that cyclists' violation of red-light regulation is a typical infringement committed by cyclists. Furthermore, an association between cyclists' crash involvement and red-light violations has been found across different countries. The literature on red-light running cyclists' behavior in relation to their characteristic is still scarce. The present study, adopted an eye-observational methodology to investigates differences in cyclists' crossing behavior at intersections, with a particular attention to their demographical characteristics. The classification of cyclists' red-light behavior in risk-taking, opportunistic and law-obeying, was adopted and re-adapted to reflect more objective behaviors, eliminating any inference or judgment. Two researchers at a time observed unobtrusively at four different intersections, during morning and late afternoon peak hours, 1381 cyclists approaching the traffic light during the red phase. More than 60% of the observed cyclists violated the traffic control. Results showed that the visual search strategy displayed by the cyclists and the presence of other cyclists at the intersection are important factors in predicting the probability of red-light running behavior.

Interventions to improve mental health can target individuals, working groups, their leaders, or organisations, also known as the Individual, Group, Leader, and Organisational (IGLO) levels of intervention. Evaluating such interventions in organisational settings is complex and requires sophisticated evaluation designs taking into account the intervention process. In the present systematic literature review, we present state of the-art of quantitative measures of process evaluation. We identified 39 papers. We found that measures had been developed to explore the organisational context, the intervention design, and the mental models of the intervention and its activities. Quantitative process measures are often poorly validated, and only around half of the studies linked the process to intervention outcomes. Fifteen studies used mixed methods for process evaluation. Most often, a qualitative process evaluation was used to understand unexpected intervention outcomes. Despite the existence of theoretical process evaluation frameworks, these were not often employed, and even when included, frameworks were rarely acknowledged, and only selected elements were included. Based on our synthesis, we propose a new framework for evaluating interventions, the Integrative Process Evaluation Framework (IPEF), together with reflections on how we may optimise the use of quantitative process evaluation in conjunction with a qualitative process evaluation.

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.