List of Online-first articles on the SJWEH website http://www.sjweh.fi/list_onlinefirst_rss.php en-US SJWEH 1 lodo@ttl.fi (Lisa O\'Donoghue-Lindy) risto@toivonen.biz (Risto Toivonen) http://www.sjweh.fi/show_abstract.php?abstract_id=4264 http://www.sjweh.fi/show_abstract.php?abstract_id=4264 Thu, 11 Dec 2025 13:07:42 +0200 Original article by Aboagye E, Botha W, Tinnerholm Ljungberg H, Bodin Danielsson C, Jensen I. doi:10.5271/sjweh.4270]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4270 http://www.sjweh.fi/show_abstract.php?abstract_id=4270 Sun, 07 Dec 2025 22:26:39 +0200 Editorial 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, 25). Empirical evidence from large-scale surveys In addition to peer-reviewed studies, the review integrated findings from major institutional reports based on large, representative samples. The EU-OSHA report (29) drew on OSH Pulse survey data covering 27 250 workers across the EU. The results suggest that each one-unit increase in ALMA intensity was associated with a 21% rise in psychosocial risks and a 16.5% increase in health issues. Similarly, a Foundation for European Progressive Studies 2023 survey of 5141 workers in Nordic countries (30) found that intensive use of ALMA nearly doubled stress levels compared with workplaces without ALMA. Worker involvement and transparency as mitigation strategies Another key finding of the review was the importance of potential “moderators” that can buffer the negative effects of ALMA. Two strategies appeared: worker involvement and transparency. In terms of worker involvement, collective worker representation has been effective in negotiating limits on algorithmic control, protecting worker privacy, and discretion (31). Participatory approaches, such as co-design and collective bargaining, can help ensure worker influence in the implementation of ALMA, supporting autonomy and trust (30). While not as impactful as direct involvement, transparency also plays a key role in mitigating the negative effects of ALMA. Clearly communicating how algorithms function and how decisions are made can help maintain job satisfaction, motivation, and trust (30). Transparency also further enhances perceptions of fairness, particularly in platform work settings (21). These two strategies are well-established in OSH practice and remain vital as algorithmic systems evolve. Research and methodological implications The findings of the review conducted in the ALMA-AI project underscore that ALMA systems often create an imbalance between job demands and available job resources, contributing significantly to psychosocial risks and negative OSH outcomes. While worker participation and transparency can help mitigate these effects, the novelty and complexity of ALMA call for continuous research, adaptive regulation, and collaboration across stakeholders. Future research should focus on effective strategies to protect OSH under ALMA, with particular attention to moderating factors such as worker participation, transparency, and the broader socio-technical context. Longitudinal studies are particularly needed to assess the long-term effects of ALMA and capture adaptation processes over time. A key methodological challenge concerns the lack of standardized and validated tools for assessing ALMA intensity, functions, and impacts, particularly in traditional workplaces. Existing instruments, such as the Algorithmic Management Questionnaire (AMQ) (32) provide a valuable foundation for measuring ALMA exposure and its OSH implications. However, a universally accepted methodology for internal risk assessment is still lacking. The AMQ requires further validation, translation, and adaptation across countries, sectors, and employment types. Some items developed for platform work, such as those related to compensation or job termination, may be less relevant in traditional organizations, whereas new dimensions, like algorithmic task allocation, may have significant psychosocial relevance. Developing robust and context-sensitive assessment tools capable of capturing the intensity, functions, and uses of ALMA systems and related practices is a fundamental research priority. Such tools would support both scientific understanding and policy development by providing a clearer basis for practice-oriented regulation and workplace risk assessment. Importantly, future studies should account for differences between platform and traditional workplaces, where ALMA systems are embedded within existing managerial structures and practices (1). Continued research is essential to refine and expand measures that safeguard workers’ rights and well-being in increasingly digital workplaces, ensuring that ALMA systems align with principles of health, safety, and fundamental rights such as privacy, equality, and non-discrimination. Policy and practice implications The findings of the ALMA-AI review (3), together with prior research (6), demonstrate that ALMA fundamentally reshapes power relations at work, with control shifting increasingly from workers to employers and platform operators. Of particular concern is the growing risk of psychosocial hazards. These risks often remain obscured, as ALMA systems may be perceived as objective or neutral due to their technical logic. In practice, however, these systems are designed for specific organizational purposes and reflect the values and assumptions of their developers and implementers. Software developers, therefore, represent an additional stakeholder group in OSH, as their design choices may consciously or unconsciously embed management logics. The impact of ALMA on workers ultimately depends on managerial choices and the broader organizational context. In many cases, employers and platform operators prioritize efficiency and profit, leading to intensified control over workers’ tasks and conditions (33, 34). This underscores the need for robust regulatory and organizational safeguards that ensure compliance with OSH standards and the protection of workers’ health and rights. A significant step in this direction has been taken with the adoption and forthcoming implementation of the EU Artificial Intelligence Act. The classification of AI systems used in worker management as “high risk” requires employers to conduct comprehensive assessments and mitigate potential impacts on OSH and fundamental rights. However, legal safeguards alone cannot address the complex organizational and psychosocial dynamics documented in empirical studies (eg, 4). OSH research therefore remains crucial in informing both regulation and workplace practice. From a policy and practice perspective, regulations should mandate OSH risk assessments for ALMA systems, whether or not they include AI. These assessments must safeguard privacy, equality, and non-discrimination, and ensure workers’ right to information and transparency in how such systems operate. Evidence from the ALMA-AI review shows that worker participation and transparency in algorithmic processes are decisive in mitigating negative impacts. Therefore, employers should adopt participatory design and consultation processes when introducing ALMA systems and ensure accountability in algorithmic decision-making. ALMA-related risks should also be incorporated into occupational risk management systems and complemented by training on psychosocial risk prevention. Finally, a coordinated European effort involving OSH institutions, researchers, policymakers, and social partners is essential to monitor and manage the psychosocial risks associated with ALMA. Such cooperation should ensure meaningful human oversight, strengthen transparency, and enhance workers’ capacity to engage in dialogue over system design and implementation. Acknowledgements The ALMA-AI project is a Partnership for European Research in Occupational Safety and Health (PEROSH) project (perosh.eu/project/alma-ai-project-exploring-the-occupational-health-and-safety-impact-of-algorithmic-management-ai-systems). Members of the project include more than those involved in this article. The project includes colleagues from multiple research organizations: Jorge Martín González (INSST, Spain); Marie Jelenko and Thomas Strobach (AUVA, Austria); Joanna Kamińska, Karolina Pawłowska-Cyprysiak and Katarzyna Hildt-Ciupińska (CIOP-PIB, Poland); Teppo Valtonen and Heidi Lahti (FIOH, Finland); Giuliana Buresti and Fabio Boccuni (INAIL, Italy); Benjamin Paty and Virginie Govaere (INRS, France); Jon Zubizarreta, Paula Lara and Denis Losada (INSST, Spain); Therese Kristine Dalsbø (STAMI, Norway); Elsbeth de Korte and Mairi Bowdler (TNO, The Netherlands). References 1. Baiocco S, Fernández-Macías E, Rani U, & Pesole A. The algorithmic management of work and its implications in different contexts. Seville: European Commission, Joint Research Centre (JRC); 2022. JRC Working Papers Series on Labor, Education and Technology, No. 2022/02. Available from https://publications.jrc.ec.europa.eu/repository/handle/JRC129749 2. European Agency for Safety and Health at Work (EU-OSHA). 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Int J Commun. 2016;10:3758–3784. by Bowdler M, Lahti H, Jelenko M, Buresti G, Valtonen T. doi:10.5271/sjweh.4263]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4263 http://www.sjweh.fi/show_abstract.php?abstract_id=4263 Fri, 05 Dec 2025 12:28:58 +0200 Original article by van Veen M, Oude Hengel KM, Schelvis RMC, Boot CRL, Veldman K, Arends I, Bültmann U. doi:10.5271/sjweh.4262]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4262 http://www.sjweh.fi/show_abstract.php?abstract_id=4262 Tue, 02 Dec 2025 17:49:39 +0200 Discussion paper by Holtermann A, Sørensen OH, Jacobsen SS, Lindberg L, Andersen LL. doi:10.5271/sjweh.4260]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4260 http://www.sjweh.fi/show_abstract.php?abstract_id=4260 Sun, 23 Nov 2025 22:12:00 +0200 Original article by Skovlund SV, Wester CT, Kyriakidis S, Brusaca LA, Andersen LL, Sundstrup E, Rasmussen CDN. doi:10.5271/sjweh.4261]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4261 http://www.sjweh.fi/show_abstract.php?abstract_id=4261 Mon, 17 Nov 2025 17:54:56 +0200 Original article by Sainiyom P, Saeangsirisuwan V, Leow CHW, Lee JKW, Surapongchai J. doi:10.5271/sjweh.4259]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4259 http://www.sjweh.fi/show_abstract.php?abstract_id=4259 Tue, 11 Nov 2025 17:55:19 +0200 Original article by Shkembi A, Linhart E, Chou S, Coulentianos MJ, Adhvaryu A, Austin-Breneman J, Nambunmee K, Neitzel RL. doi:10.5271/sjweh.4257]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4257 http://www.sjweh.fi/show_abstract.php?abstract_id=4257 Tue, 04 Nov 2025 12:32:10 +0200 Original article by Thern E, Jonsson E, Elling DL, Almroth M. doi:10.5271/sjweh.4255]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4255 http://www.sjweh.fi/show_abstract.php?abstract_id=4255 Tue, 14 Oct 2025 13:58:51 +0200 Original article by Hazak A, Kantojärvi K, Sulkava S, Kukk M, Jääskeläinen T, Salomaa V, Koskinen S, Perola M, Paunio T. doi:10.5271/sjweh.4256]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4256 http://www.sjweh.fi/show_abstract.php?abstract_id=4256 Wed, 08 Oct 2025 17:51:44 +0200 Original article by Väisänen D, Ekblom-Bak E, Eriksson L, Kallings LV, Svartengren M, Lundmark R, Lindwall M, Blom V, Stenling A. doi:10.5271/sjweh.4254]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4254 http://www.sjweh.fi/show_abstract.php?abstract_id=4254 Thu, 02 Oct 2025 21:28:39 +0200 Original article by Aegerter AM, Johnston V, Volken T, Sjøgaard G, Ernst MJ, Luomajoki H, Elfering A, Melloh M, NEXpro collaboration group. doi:10.5271/sjweh.4243]]> http://www.sjweh.fi/show_abstract.php?abstract_id=4243 http://www.sjweh.fi/show_abstract.php?abstract_id=4243 Sat, 09 Aug 2025 17:12:23 +0200 Original article eff) exceeding WBGT reference (WBGT_ref). Outdoor and indoor WBGT were determined using historical, region-specific hourly meteorological data (temperature, radiation, humidity, wind speed) across Europe, between 1970 and 2024. WBGT values were adjusted for job-specific clothing to obtain WBGT_eff. WBGT_ref was based on metabolic rate, calculated using body surface area and job-specific physical activity, and adjusted for acclimatization status. Further adjustments were made for the job title-specific presence of local heat and cooling sources, time spent indoors versus outdoors, and working schedules. Results The number of annual hours workers experience heat stress is highest among jobs involving local heat sources and physical demanding tasks, especially when work clothing is mandatory. Southern Europe has a higher annual heat stress burden compared to other regions. Exposure varies across calendar years and is substantially higher among unacclimatized versus acclimatized workers. Conclusions Incorporating job-, region-, and year-specific factors, the heat JEM provides a harmonized tool for studying occupational heat stress. Its transparent framework allows for updates with new data and extensions to other years and regions. by de Crom TOE, Scholten B, Traini E, van der Sanden K, Kingma B, Pekel F, Ghosh M, Notø H, Turner MC, Alba Hidalgo MA, Klous L, Albin M, Kolstad HA, Selander J, Calvin Ge C, Pronk A]]>