In recent years, algorithmic management (ALMA) systems have spread rapidly from platform work to more traditional sectors such as logistics, retail, and healthcare (1, 2). Defined as “the use of complex digital systems or AI [artificial intelligence] to manage workers” (3) and encompassing the semi- and fully automated execution of managerial functions such as scheduling, goal setting, and performance evaluation (4), ALMA is profoundly reshaping how work is organized and monitored. Building on recent discussions in this journal on digitalization and occupational health (eg, 5), this editorial examines how ALMA is transforming working conditions and power dynamics and considers its implications for workers’ health and well-being in changing work environments. A review of the literature up to 2022 (6) synthesized emerging evidence on how ALMA affects job quality and worker health in platform work, highlighting psychosocial risks such as intensified workload, irregular and unpredictable working hours with unpaid waiting times, reduced decision authority, and social isolation. In other words, ALMA can recalibrate the balance between job demands and resources in ways that heighten psychosocial risks. Examined in the light of established models of psychosocial risk (7) – such as the Job Demands–Resources model (8, 9), the Job Demand–Control–Support model (10,11), and the Effort–Reward Imbalance model (12,13) – these prior findings suggest that ALMA can create conditions that trigger classic risk pathways in which job demands exceed available resources, leading to stress, burnout, and related health outcomes. However, ALMA is not inherently detrimental. A growing body of research emphasizes that ALMA is not just a technical system, but a socio-technical process shaped by organizational contexts and human decisions (1, 4, 14). Thus, ALMA`s implications for work and workers depend not only on technical features but also, for instance, how and for what purpose the systems are used. When designed with transparency, fairness, and the opportunity for human influence over the system (4), the harmful effects of ALMA might be mitigated. Some researchers have even underscored the dual effect of ALMA, noting that algorithmic systems can both constrain and enable autonomy and value to workers depending on their design (15). However, evidence for positive outcomes remains limited and largely theoretical (4). While some studies have pointed to potential benefits for workers, such as increased flexibility in deciding working hours, these are often accompanied by new forms of control (16). This editorial builds on a structured (non-systematic) literature review conducted within the PEROSH network (3), which followed a systematic search process and was visualized according to PRISMA 2020 reporting principles (17). The review synthesized findings from 39 studies (published 2022–2024) on the Occupational Safety and Health (OSH) implications of ALMA, covering both traditional and platform work. While the resulting report itself was not peer-reviewed, it draws exclusively from high-quality empirical sources (33 peer-reviewed scientific studies and 6 grey literature reports) and was developed by a team of researchers across multiple European institutions (3). Based on the review, we argue that ALMA is not merely a digital enhancement of existing managerial practices. Rather, it constitutes a novel form of work organization that shifts managerial decision-making from humans to algorithms, thereby reshaping the psychosocial work environment and redistributing power, control, and responsibility. The following sections summarize the key findings of our review, discuss their implications for policy and practice, and outline research priorities to ensure that ALMA supports rather than undermines workers’ health and well-being. Key findings of the review The ALMA-AI project’s review indicated that the use of ALMA systems frequently leads to excessive job demands while simultaneously reducing key job resources needed to manage those demands. This imbalance shapes working conditions in ways that heighten psychosocial pressures and increase the risk of adverse OSH outcomes. In the ALMA-AI report, these patterns were identified and subsequently structured into both quantitative and qualitative studies, spanning across platform work and traditional workplaces. Intensification of job demands Quantitative analyses conducted in platform work settings consistently show that ALMA systems generate psychosocial – particularly time – pressures that significantly elevate work-related stress levels (eg, 18–21). Time pressure as a job demand was also referred to in qualitative studies that identified excessive workloads as a recurrent feature of ALMA across various platform sectors (eg, 22, 23). In some accounts, workers even described how these heightened demands made them feel “exploited” (24). Similar findings were highlighted in an ILO report, where the intensification of work appeared to be directly linked to the use of monitoring systems (25). Depletion of job resources The review showed that the negative impact of ALMA on OSH is often exacerbated when these systems undermine key job resources; a pattern observed particularly when ALMA is used as a control mechanism. Such effects include reduced autonomy (26) and diminished social support, manifested for instance as limited time to interact with co-workers (25). ALMA often imposes standardized workflows, reducing opportunities for worker discretion (27). The loss of autonomy is especially pronounced when algorithms are perceived as opaque (26) or used to impose strictly timed or closely monitored tasks (28). Some of the reviewed studies also reported that workers frequently feel excluded from decision-making processes surrounding the introduction and use of ALMA systems (eg,...

For years, the connection between safety behaviours and injury and illness in high-risk industries has been recognised, but the effectiveness of this link has been somewhat overlooked. Since there is still a significant amount of injury within high-risk workplaces, this systematic review aims to examine the effectiveness of behavioural interventions to decrease fatal and non-fatal injuries within high-risk industries. Scopus and Google Scholar were used to find relevant systematic reviews and meta-analyses on this topic. In total, 19 articles met the inclusion criteria. Of these articles, 11 suggested that their reviewed interventions revealed some evidence of being effective in reducing injury/accident rates. Additionally, seven of the papers found that the interventions affected certain determinants, such as safety knowledge, health and safety behaviours, attitudes, efficacy, and beliefs. One of the papers found no effect at all. It must be noted that a significant amount of the articles (n = 10) reported methodological quality or quantity issues, implying that the results should be approached with caution. Nonetheless, it was found that certain components, such as multi-faceted interventions tailored to the target group, contribute to either reducing injury/accident rates or improving the specific aforementioned determinants. There is a need for additional safety interventions in high-risk industries that are based on methodologically sound structural elements and theoretical frameworks. Existing approaches, such as Intervention Mapping, can assist safety professionals in achieving this goal.