Journal of Health and Safety at Work

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Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models.
Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.
Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.
Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.
 

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Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance.
Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems.
Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%.
The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%.
Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.
 

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Introduction: Due to uncertainties regarding the risks of engineered nanomaterials for human health and the environment, different organizations and researchers have developed various management frameworks and assessment tools to mitigate hazards during the procedures and applications of engineered nanomaterials. However, most of these techniques do not meet all the individual requirements. This study provides a review and introduction to the techniques developed for the management of safety, health, and environmental risks associated with engineered nanomaterials.
Material and Methods: In order to find pertinent documents on the safe handling of engineered nanomaterials, a search was conducted using the following keywords: “Engineered nanomaterials”, “Framework”, “Tool”, “Risk management”, “Occupational exposure”, “Environment”, “Risk assessment”, and “Nanotechnology”. The search was conducted on various databases, including Scopus, Web of Science, NIOSH, ECHA, and ISO. Among the search results, tools and frameworks that specifically focus on the safety, health, and environmental risk management or assessment of engineered nanomaterials were selected.
Results: Among the search results, 17 frameworks and 11 developments in the field of managing occupational, environmental, and toxicological risks associated with engineered nanomaterials were discussed. Various frameworks and tools for identifying, evaluating, and managing the potential risks of engineered nanomaterials vary in terms of their scope, goals, risk assessment approaches, and output, offering diverse applications.
Conclusion: Various tools and frameworks, each with unique properties, applications, and limitations, can assist organizations in achieving their goals related to safety, health, and environmental issues in the field of nanotechnology. Currently, there is no consensus on the optimal approach for assessing the risks of nanomaterials, underscoring the necessity for additional research, development, and collaboration in this field.
 

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Introduction: Driving has various harmful factors due to its nature, which affect drivers’ health directly and indirectly. Therefore, it is necessary to know the situation and prevalence of these factors in drivers to implement preventive measures.
Material and Methods: This cross-sectional study is a part of a cohort study conducted (2016 to 2018) among the professional drivers of Shahroud, Iran. Data related to background information, blood pressure, height, weight, waist circumference, body mass index, blood factors, hearing loss (dB), respiratory performance indicators, sleep disorders, and accidents were collected from the participants with standard tools and methods.
Results: This study examined 1461 male professional drivers with an average age of 37.30±6.96 years. A total of 426 participants had metabolic syndrome. 797 and 942 people had different degrees of hearing loss, respectively, in the right and left ear. About 129 people had obstructive sleep apnea, and 1330 people had insomnia. Investigations showed that 351 drivers had at least one accident.
Conclusion: This study showed the prevalence of health risk factors in professional drivers at the examined time point. Considering the vital role of drivers in transportation and the country’s economy, it seems necessary to pay more attention to the health of this occupational group. Regular health screening, healthy lifestyle training, improvement of working conditions, and stress management are some interventions that can effectively improve drivers’ health.
 

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Introduction: Given the importance of the employment status of graduates for countries, the current study aims to investigate the employment status of occupational health and safety engineering (OHS) graduates from Kashan University of Medical Sciences (KAUMS), from the establishment of the field in 1996 up until 2023
Material and Methods: The cross-sectional study examined the employment status of OHS graduates of KAUMS using a researcher-developed Google form questionnaire. The questionnaire included demographic characteristics, five questions for unemployed individuals, and 60 questions for employed individuals. Finally, descriptive and analytical analyses of the study were performed using SPSS v16 software. 
Results: A total of 229 graduates participated in this study. The results regarding the frequency of employed and unemployed participants showed that 198 (86.5%) participants were employed, and 31 (13.5%) were unemployed at the time of the study. Most employed participants (46%, or 90 individuals) work in the industry and mining sector. Evaluating the effectiveness of the educational course of employed participants in relation to their jobs revealed that 88 participants (44.4%) believe that the subjects taught in the courses are moderately compatible with work needs. 
Conclusion: With the advancement of industries and the expansion of production, the need to control harmful factors and improve workers’ health is more evident than ever before, making it likely that graduates of this field will have favorable job prospects in the future. On the other hand, the academic conditions should be improved, particularly regarding the quality of the educational and curriculum programs of KAUMS. 
 

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Introduction: The increasing advancement of technology in the field of digital technology and automation has led to a change in the line of work and job content, and it seems that organizations are pushing their employees towards harder and longer work, which affects the health of employees. This study investigated the impact of job demands and resources on overtime and work-related health through the mediation of workaholism and work engagement.
Material and Methods: The work is a descriptive survey in terms of purpose and nature. The study’s statistical population consisted of employees of Isfahan Metro Company (320 people), of which (175 people) were studied by Simple Random Sampling. To collect the required information, Spence and Robbin’s (1992) Addiction Questionnaire, Lodahl and Kejner’s (1965), Langseth-Eide’s (2019) Perceived Health-Related Questionnaire, and Bryson Bangers’ Caricature Content (1998) were distributed and supplemented based on a Likert scale. A Structural equation model (SEM) was used to test the hypotheses.
Results: The results showed that job demands on workaholism (β=0.394, T=5.969, P <0.05) and job resources had a positive and significant relationship with work engagement (β =0.502, T =7.832, p <0.05), and workaholism and work engagement on the relationship between job demands and resources over time and health. Mediating work-related perceptions (Z= 4.383, Z= 2.189, Z= 3.797). In addition, job resources did not moderate the relationship between job demands and workaholism (β= -0.049, T =1.333, p> 0.05). 
Conclusion: The results of this study showed that there is a distinction between workaholism and work engagement as two different types of hard work (i.e., negative and positive) in the health process in the JD-R model. 

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Introduction: Workplaces often contain potential risks, such as exposure to toxic chemicals. Conducting a thorough health risk assessment helps employers recognize these dangers and implement necessary controls. In the 20th century, modern risk assessment frameworks began to be established with the rise of public health agencies.
Material and Methods: The present study is a narrative review. In order to obtain necessary information, Persian and English texts were searched in Web of Science, PubMed, Scopus, SID and Magiran databases. Keywords such as “health risk assessment”, “chemicals” and “nanomaterials” were used in this study.
Results: Both quantitative and qualitative health risk assessments play critical roles in occupational health, with each method providing different levels of depth and accuracy depending on the situation. EPA Model, Monte-Carlo Simulation, Physiologically Based Pharmacokinetic (PBPK) Modeling, Quantitative Structure-Activity Relationship (QSAR) Models, Probabilistic Risk Assessment (PRA), Life Cycle Impact Assessment (LCIA), and Biologically Based Dose-Response (BBDR) Models, are among the most important quantitative methods for assessing the health risk of chemicals. COSHH Model, ICCT Model, ICMM Model, Australian Model, and Romanian Model, are the most important qualitative methods for health risk assessment of chemicals. In addition to the quantitative and qualitative methods, semi-quantitative methods like Singapore Model, LEC Method, and SEP Model, have also been proposed for assessing the health risk of chemicals. The preference for qualitative over quantitative methods in the risk assessment of activities involving nanomaterials stems from substantial uncertainties, limited data availability, and the unique and complex behaviors of nanomaterials in the workplaces.
Conclusion: Overall, the evolution of health risk assessment methods reflects a continuous drive towards greater accuracy, reliability, and relevance. As we continue to innovate and expand our knowledge, the field is well-positioned to address the complex and evolving landscape of chemical and material risks, ensuring the protection of human health and the environment. 

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Introduction: Mental health in the workplace is a critical factor influencing both employee well-being and organizational productivity. However, issues such as stress, anxiety, depression, and burnout can significantly impair job performance and overall quality of life. While numerous tools have been developed to assess mental health, many are not specifically designed or updated for work environments. As the complexity of the workplace continues to increase, accurate identification and evaluation of these conditions become increasingly essential. This study aims to review available tools and identify the most proper methods for screening and assessing mental health issues in the workplace.
Material and method:  A scoping review approach was used to identify mental health assessment tools applicable to workplace settings. The PubMed, PsycINFO, Web of Science, and Scopus databases were searched using keywords related to mental health at work. Studies published after 2020 were included, focusing on tools developed and validated in work environments. Two authors independently extracted and reviewed data from selected studies. Tools were categorized based on their aims and specific characteristics.
Results: After screening, 12 studies were selected from a primary set of 746 papers.. The extracted tools were designed to assess stress, anxiety, depression, and burnout. While most tools were developed for general settings or the public, some were appropriated to specific occupational groups, such as military personnel and healthcare workers. Burnout assessment tools were the most frequently referenced category.
Conclusion: Newer tools, such as the Work Stress Screener, Occupational Depression Inventory, and Burnout Assessment Tool, offer potential advantages over older instruments. Shorter, specialized tools are recommended to assess job anxiety effectively. Organizations should prioritize selecting tools that align with their employees' specific working conditions to promote mental health and productivity.

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Introduction: This study aimed to profile Iran’s occupational health services and identify the challenges and implementation strategies, focusing on the coverage of these services and interventions.
Material and Methods: This mixed-method research utilized a triangulation approach to generate qualitative data through document reviews from international organizations such as the WHO and ILO, examinations of national occupational health systems, laws and regulations, previous studies, and interviews. Quantitative data were collected from a national portal using a standard inspection checklist and secondary data from the National Statistics Center. Qualitative data were analyzed using both inductive and deductive content analysis, while quantitative data were analyzed using descriptive statistics.
Results: The occupational health services profile for Iran comprised 45 indicators across 9 areas and 6 types. The coverage of inspection services for identified workplaces and workers was 93% and 92%, respectively. However, the estimated coverage of inspection services for all existing workplaces and workers was only 39% and 15%. Among the included workplaces, access to full health facilities was at 48%, while occupational exposure control was at 18%. For the covered workers, the coverage rates for occupational medical examinations, occupational health training, and utilization of personal protective equipment were 58%, 63%, and 66%, respectively.
Conclusion: The key intervention for improving service coverage and stability—aside from inspections, which are governed—lies in outsourcing services to various providers. This approach involves removing existing barriers and enhancing service provision for small workshops. Additionally, redesigning occupational health services should focus on modifying educational curricula, research, and implementation programs, emphasizing the economic aspects of controls, and prioritizing low-cost and effective measures, especially in small workplaces.

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Introduction: Employee health is a critical predictor of organizational productivity. Accordingly, the purpose of this research was to investigate the effects of reverse mentoring and job crafting on mental and physical health, with engagement playing a mediating role.
Material and Methods: This research employed a descriptive survey design with an applied focus, conducted cross-sectionally using survey methods for data collection. The statistical population consisted of 330 employees from the Amirkabir Kashan Steel Company. A sample size of 180 was determined using Cochran’s formula, and a simple random sampling method was utilized. Data were collected via a standardized questionnaire comprising 63 questions. The questionnaire’s validity was confirmed through convergent and divergent validity tests, and its reliability was supported by Cronbach’s alpha, which exceeded 0.7 for all variables. Data analysis was performed using descriptive statistics (SPSS) and inferential statistics (PLS).
Results: The findings confirmed all hypotheses and demonstrated the appropriate fit of the research model. Results revealed significant impacts of reverse mentoring on mental health (β = -0.482, t = 5.899) and physical health (β = 3.460, t = 4.430), as well as job crafting on mental health (β = -0.545, t = 6.193) and physical health (β = -0.756, t = 8.979) among employees and managers of the Amirkabir Kashan Steel Company. Moreover, engagement was found to mediate the effects of reverse mentoring on mental health (β = -0.510, t = 5.948) and physical health (β = -0.242, t = 3.799). Engagement also mediated the effects of job crafting on mental health (β = -0.345, t = 4.186) and physical health (β = -0.405, t = 4.751).
Conclusion: The results indicate that by implementing reverse mentoring and job crafting methods, managers can effectively control engagement levels, thereby maintaining organizational productivity and improving the health and well-being of employees. 
 

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Introduction: The Total Quality Safety Management (TQSM) model is a tool used to assess the performance of occupational health and safety (OHS) management. This study evaluated the OHS management performance in two edible oil production factories using the TQSM model.
Material and Methods: A total of 78 OHS-related evaluation criteria across four main areas of the TQSM model were assessed at the Saboos Mazand (Factory 1) and Mino Caspian (Factory 2) factories, located in Mazandaran Province, Iran. The four areas included Total Quality Management (TQM), the ISO 9001 Quality Management System guidelines, the Voluntary Protection Program (VPP), and Process Safety Management (PSM). The study population consisted of 20 individuals from each factory, including managers, supervisors, and members of the OHS Committee, all with at least one year of experience in their respective roles. Participants were selected using a convenience sampling method.
Both factories had established active management systems for quality management (ISO 9001), customer satisfaction management (ISO 10002), and Hazard Analysis and Critical Control Points (HACCP) at the time of the study, reflecting the nature of the industry.
Results: The total scores achieved by Factory 1 and Factory 2 across the four main areas were as follows: 52.11 and 51.8 in the TQM area, 43.94 and 45.5 in the QMS-ISO 9001 area, 45.23 and 46.45 in the VPP area, and 30.22 and 30.06 in the PSM area. The overall scores obtained in the TQSM model were 171.51 for Factory 1 and 173.81 for Factory 2, corresponding to 54.97% and 55.7% of the maximum achievable score, respectively. No significant difference was observed between the mean scores across the four areas of the TQSM model for the two factories (p > 0.05).
Conclusion: Both factories exhibited an overall average performance level in OHS management. The evaluation criteria across the four areas of the TQSM model indicated weak to moderate conditions in the assessed subareas. Effectively using this model highlighted the organizational and operational areas requiring increased effort and focus to enhance OHS management performance in both factories.

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Introduction: Safety in healthcare facilities is critically important for the health and well-being of employees, patients, and organizational effectiveness. In recent years, various studies have examined the relationship between leadership styles or approaches and safety performance as one of the indicators of safety promotion. The present systematic review examines the relationship between different leadership styles and the safety performance of employees in healthcare facilities to provide a better understanding of the positive or negative effects of leadership on safety and to suggest strategies for improving safety performance in healthcare facilities. 
Material and Methods: A search was conducted in Scopus, PubMed, and Web of Science (ISI) databases. Keywords related to leadership, safety performance, and healthcare employees were used. Studies published up to the end of 2024 were identified and reviewed in accordance with PRISMA guidelines.
Results: Nineteen relevant papers were selected and included in the study. During the review of studies, eleven leadership styles or approaches were identified in relation to safety performance in various healthcare facilities. These included transformational leadership, leader-member exchange leadership, leader safety priority communication and feedback, ethical leadership, empowering leadership, inconsistent and destructive leadership, transactional leadership, task-oriented leadership, authentic leadership, safety leadership, and servant leadership. All leadership styles except for inconsistent and destructive leadership had a direct or indirect positive effect on safety performance. Also, the largest number of studies (n=4) focused on transformational leadership style and leader-member exchange leadership.   
Conclusion: The selection of appropriate leadership styles can contribute to enhanced safety, a reduction in occupational incidents, and improving service quality in healthcare settings. The findings of this study highlight the importance of developing effective leadership styles and strengthening appropriate managerial approaches to improve safety in healthcare facilities. 

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Introduction: Artificial Intelligence (AI) and digitalization are pivotal in enhancing Occupational Health and Safety (OHS), reducing workplace accidents, improving conditions, and boosting organizational productivity. This study examines the impacts, challenges, and opportunities of these technologies in workplace safety.  
Material and Methods: A narrative review was conducted via databases (Google Scholar, PubMed, IEEE Xplore, ScienceDirect) using keywords like “AI in occupational safety” (2013–January 2025). After screening 125 articles, 71 met the inclusion criteria (Persian or English publications). Qualitative content analysis identified key challenges and opportunities.  
Results: Artificial intelligence has been used in predicting incidents, monitoring, process optimization, and analyzing OHS challenges. By analyzing historical data and hazard patterns, AI enables proactive risk mitigation. Continuous learning in AI models enhances predictive accuracy and environmental adaptability. However, data quality issues persist; techniques such as transfer learning offer potential solutions. AI-driven automation reduces human error, yet challenges include ethical concerns and infrastructure gaps.
Conclusion: AI and digital technologies are transforming OHS through predictive analytics and real-time surveillance. To fully leverage these benefits, future efforts must focus on addressing data quality issues, establishing robust ethical frameworks, and developing advanced infrastructure. Further research is essential for the practical implementation of AI in a variety of work environments.

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Introduction: Surgical technologists encounter a challenging work environment, and therefore, they require well-organized workplaces and appropriate health-related training. This study aimed to determine the effect of a participatory ergonomics program on musculoskeletal disorders (MSDs) and general health among surgical technologists
Material and Methods: This single-blind randomized controlled trial was conducted in 2023-2024 in operating rooms of teaching hospitals in Isfahan, Iran. The study population comprised 88 surgical technologists meeting the inclusion criteria. One hospital was randomly selected as the intervention site, while the remaining hospitals served as the control group. Data were collected using a demographic questionnaire, the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), and the 28-item General Health Questionnaire (GHQ-28). Initially, all participants in both groups completed baseline assessments. The intervention group received a participatory ergonomics program. Follow-up assessments were conducted at 3 and 6 months post-intervention in both groups. Data were analyzed using SPSS version 24 employing both descriptive and inferential statistical methods, with a significance level set at 0.05.
Results: Before the intervention, no significant differences were observed between the intervention and control groups in terms of mean scores for general health and MSDs (p=0.55). Three months post-intervention, a significant difference was found in the mean score of lower extremity MSDs between the groups (p=0.033). Six months post-intervention, a significant difference was observed in the mean score of Trunk region MSDs between the groups (p=0.038). Significant differences in mean general health scores were observed between the groups at 3 months (p=0.001) and 6 months (p=0.001) post-intervention, with the intervention group reporting better general health compared to the control group.
Conclusion: The implementation of a participatory ergonomics program can improve general health and reduce MSDs among surgical technologists.