Journal of Health and Safety at Work

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Introduction: With the rapid development of new chemicals across various industries and the growing need for efficient and accurate toxicity assessments, in silico methods have emerged as a screening tool due to their cost-effectiveness, time efficiency, and reduction in animal testing. The aim of this review is to examine the existing studies on the application of in silico methods in predicting the toxicity of chemical compounds in occupational and industrial settings.
Material and Methods: This systematic review follows established protocols and is based on data extracted from reputable scientific databases such as PubMed, Scopus, and Web of Science. The review analyzes articles published between 2000 and 2024 that utilized in silico methods for toxicity prediction in occupational toxicology. Inclusion criteria focused on studies that applied modeling, simulation, and prediction methods primarily to chemical toxicity in workplace environments. Also, the quality assessment of the articles was done using the STROBE form.
Results: This study surveyed 13 articles on computer simulation of chemical compounds from 2000 to 2024. The majority of research was conducted between 2020 and 2024. The reviewed articles, based on the STROBE form, had a moderate to high quality. Various methods, including Quantitative Structure-Activity Relationship (QSAR), machine learning, and molecular dynamics, were widely used to predict the toxicity of chemical compounds, with the predictive accuracy of these models generally being high. The results also indicated that QSAR methods had the most application in studies predicting the toxicity of chemical compounds used in industries.
Conclusion: In silico methods, using molecular descriptors and structural data, have shown high accuracy in predicting toxicity. However, challenges such as limitations in reliable data, the need for model improvement, lack of experimental data, and the complexity of chemical interactions exist. The results indicated that the use of computational methods can significantly reduce the need for animal testing and improve risk assessment. These studies also emphasize the importance of improving and developing predictive models to enhance their accuracy and applicability. Overall, it can be said that modeling can serve as an effective tool in reducing costs and improving safety in workplace environments. 
 

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Introduction: Work-family conflict can lead to negative outcomes such as psychological distress in all employees, especially female nurses, as they face unique occupational challenges. However, the specific aspect of work-family conflict that contributes most significantly to psychological distress, as well as the underlying mechanisms involved, has not been thoroughly examined. The present study aimed to investigate the relationship between work-family conflict dimensions and psychological distress.
Material and Methods: This descriptive-analytical study was conducted in 2022 on 277 female nurses employed at public hospitals in Bushehr Province. The data collection tool consisted of work–family conflict and psychological distress questionnaires. The data were analyzed using SPSS v. 19 and PLS Graph v. 3, with path analysis.
Results: The direct effects of two dimensions of work-family conflict, namely “the interference of work with personal and family life” and “the interference of family life with work,” on psychological distress were found to be statistically significant (p < 0.05). In addition, the mediating effect of the “work interference with personal and family life” on the relationship between “insufficient facilities and support” and psychological distress was statistically significant (p < 0.05). Meanwhile, the mediating effect of the “family dissatisfaction” in the relationship between “the interference of work with personal and family life” and psychological distress was not statistically significant (p > 0.05).
Conclusion: The interference of work with personal and family life, and the interference of family life with work, were identified as significant factors that directly affect psychological distress. In addition, “insufficient support and facilities” can increase “the interference of work with personal and family life”, thereby leading to greater psychological distress among female nurses. Planning to enhance support measures in both the workplace and personal life can help reduce the negative consequences of work-family conflict, such as psychological distress in female nurses. 

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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.

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Introduction: Environmental and health concerns regarding synthetic sound absorbers necessitate natural, sustainable alternatives. Agricultural waste like walnut shells is promising due to its inherent porosity. This study evaluates the acoustic properties of walnut shell composites, investigating the influence of key design parameters like thickness and chopping level on sound absorption performance. 
Material and Methods: Porous granular samples were fabricated from walnut shells at three chopping levels (minimally, moderately, finely) and four thicknesses (20, 40, 60, and 80 mm). The sound absorption coefficient was measured via the impedance tube method. Field Emission Scanning Electron Microscopy (FESEM) analyzed the material’s morphology, and results were validated with Slanted Slit (SS) and Non-uniform Pore Size Distribution (NUPSD) mathematical models.
Results: Both increased thickness and chopping level significantly enhanced sound absorption. For finely chopped samples, increasing thickness from 20 to 80 mm shifted the absorption peak from 2000 Hz to 630 Hz. At a constant 80 mm thickness, intensified chopping boosted the absorption coefficient at 630 Hz from 0.48 to 0.97. This improvement correlated directly with increased density, tortuosity, and airflow resistivity. Model predictions showed the best agreement for the most finely chopped samples.
Conclusion: Walnut shell waste, especially after intensive mechanical processing, is a highly effective and sustainable sound-absorbing material. The chopping process optimizes the acoustic structure by activating the material’s inherent micro-porosity, yielding excellent performance in the speech frequency range (500-2000 Hz). This material shows significant potential as a green alternative to synthetic absorbers for indoor noise control. 

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Introduction: Occupational noise is considered as an emerging risk factor for type 2 diabetes. Although previous studies have mainly examined the auditory outcomes of noise exposure and estimated the relative risk of diabetes, there is insufficient evidence to estimate the prevalence of type 2 diabetes in Workers exposed to occupational noise. The present study aimed to conduct a systematic review and meta-analysis of the pooled prevalence of type 2 diabetes in workers exposed to occupational noise.
Material and Methods: In this systematic review and meta-analysis, the Web of Science, Scopus, Medline (PubMed) databases and Google Scholar engine were searched up to May 2025. The quality of studies was assessed using the Newcastle-Ottawa scale. The pooled prevalence was estimated using the DerSimonian and Laird random effects model, and heterogeneity was assessed using the I² index. The pooled prevalence was reported in subgroups based on the method of diabetes diagnosis and type of study.
Results: Out of 1,193 initially identified studies, 14 studies with a total of 94,975 participants were included in the systematic review and meta-analysis. The pooled prevalence of type 2 diabetes among individuals exposed to occupational noise was estimated at 5.91% (95% CI: 4.85%–6.98%). Significant statistical heterogeneity was observed among studies (I² = 98%, p < 0.001). The subgroup analysis indicated that the prevalence of diabetes in studies based on clinical or paraclinical diagnostics was higher than in studies based on self-report (7.31% and 3.73%, respectively). Moreover, the prevalence of diabetes in cross-sectional studies was higher than in cohort studies (6.45% and 5.67%, respectively).
Conclusion: The findings indicated a moderate prevalence of diabetes among people exposed to occupational noise. This prevalence was based on preliminary studies with an acceptable level of quality. The findings highlight the importance of recognizing metabolic consequences of occupational noise exposure alongside its well-known auditory effects. 

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Introduction: Noise‑Induced Hearing Loss (NIHL) is one of the major occupational health concerns. Prolonged exposure to high noise levels not only damages auditory function but also contributes to systemic physiological disorders. This study employed the dual predictive and diagnostic capabilities of a Bayesian Network (BN) to explore the complex interactions between causal factors of NIHL and the physiological outcomes of occupational noise exposure.
Material and Methods: In this cross‑sectional study, medical and environmental records of 828 petrochemical workers were collected, including demographic, audiometric, noise, hematological, and biochemical variables. After preprocessing, an inferential BN model was developed using the Bayesian Search algorithm, enabling both Forward Inference (FI, predictive) and Backward Inference (BI, diagnostic) reasoning. Model performance was validated through Receiver Operating Characteristic (ROC) curve analysis and sensitivity testing.
Results: The FI results showed that exposure to SPL levels above 85 dB increased the risk of severe NIHL (warning level) from 9% to 57%. Also, the probability of systolic hypertension, the FBS above 100 mg/dL, and the total cholesterol above 200 mg/dL increased from 6%to10%, 8%to18%, 5% to 9% respectively. When multiple high‑risk conditions (e.g., high SPL, long work experience, noisy units) were combined, the probability of severe NIHL exceeded 70%, accompanied by cumulative metabolic disturbances. BI results indicated that the presence of severe NIHL significantly increased the posterior probability of previous exposure to high or borderline SPL levels. Moreover, metabolic indices such as triglycerides (TG) and fasting blood sugar (FBS) showed positive associations with noise exposure, even below conventional action thresholds.
Conclusion: Bayesian networks provide a powerful framework for identifying and modeling direct and indirect probabilistic dependencies between occupational noise exposure and health outcomes in industrial environments. Their bidirectional inference ability (FI and BI) enhances predictive surveillance, early diagnosis, and the design of evidence‑based preventive strategies in occupational health management.

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Introduction: Medical imaging modalities such as MRI and CT scans are indispensable for accurate diagnosis, yet they pose substantial operational and patient safety risks—particularly in resource-limited healthcare systems.
Material and Methods: This applied methodological study, conducted from July 2024 to April 2025, used a four-phase methodology: scoping, data collection, framework development, and risk analysis. Data were gathered through FGDs involving radiologists, technicians and HSE experts and also with semi-structured interviews and process mapping, which identified 125 failure modes across nine workflow stages. PFMEA assessed operational risks, whereas HFMEA focused on patient-centric hazards. A composite risk indicator that comprising 40% PFMEA RPN and 60% HFMEA hazard score, prioritized risks. Statistical analyses, including Shapiro-Wilk, Spearman’s correlation and Kruskal-Wallis tests, were used to evaluate risk distributions and inter-stage variability.
Results: The framework identified critical risks, such as insufficient operator training and staff fatigue, with post-process management and image reconstruction as high-risk phases. MRI and CT units showed distinct yet overlapping risk profiles that show significant inter-stage variability (p<0.001). The hybrid model integrated operational and clinical perspectives, which outperformed standalone FMEA methods.
Conclusion: This hybrid PFMEA-HFMEA framework offers a scalable and context-sensitive approach to enhance patient safety with operational resilience in medical imaging. Further studies should authenticate the framework in different settings and investigate long-term mitigation strategies to enhance radiology risk management.

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Introduction: Dentistry is considered a highly stressful profession due to its nature, placing dentists at an increased risk of occupational burnout and musculoskeletal disorders. This study aimed to investigate stress, occupational burnout, and musculoskeletal discomfort among dentists, as well as the role of ergonomics in reducing these problems.
Material and Methods: This study is a systematic review of articles published from 2000 to March 2025 in three databases: Scopus, Web of Science, and PubMed. The inclusion criteria required original research in English—experimental, observational, or conference-based—addressing both the prevalence of occupational stress, burnout, and/or WMSD in dentists and the impact of ergonomic interventions. Unrelated articles, review papers, books, letters to the editor, and book chapters were excluded.
Results: Out of 366 identified articles, 28 met the inclusion criteria. The most frequently reported discomforts were in the neck, lower back, shoulders, wrists, upper back, forearms, and arms, respectively. Dentists with a higher risk of occupational burnout reported more health complaints, and patient care was identified as the main source of stress. Moreover, the use of ergonomic aids—such as dental magnification loupes, optimized hand tool designs, and prismatic glasses—played a significant role in reducing musculoskeletal discomfort among dentists.
Conclusion: Stress, burnout, and musculoskeletal disorders are common challenges in the dental profession. Strong evidence supports the effectiveness of ergonomic interventions in reducing the physical burden of these problems; however, implementation faces barriers such as high costs and insufficient training. Therefore, it is recommended that ergonomic principles and the use of assistive tools be integrated as essential components of dental education curricula and ongoing professional development programs.