Background: Emergency medical personnel play a crucial role in saving the lives of patients and injured individuals before they receive hospital services. Given their working conditions and environment, emergency medical service personnel are at risk for mental health issues and musculoskeletal disorders. Therefore, this study aimed to investigate the relationship between stress, anxiety, and depression with musculoskeletal disorders among emergency medical personnel in Qom Province.
Methods: This descriptive-analytical cross-sectional study was conducted among 177 emergency medical service personnel in Qom city, selected through simple random sampling. Data collection tools included demographic questionnaires, the Depression Anxiety Stress Scales (DASS), and a body map chart. Data analysis was performed using descriptive statistical tests and multivariate structural equation modeling techniques, as well as Cronbach’s alpha coefficients and average variance extracted (AVE) analysis, with the help of SPSS version 22 and AMOS version 20 software.
Results: The average age of the participants was 33.03 ± 7.48 years. Most participants (84%) reported symptoms of musculoskeletal disorders. The reliability and validity of the DASS questionnaire and its subscales were confirmed with acceptable Cronbach’s alpha coefficients (0.7) and average variance extracted (AVE) values (0.4). Levels of stress, anxiety, and depression were above average. No significant relationship was found between stress, anxiety, and depression and musculoskeletal disorders, and demographic variables did not moderate the relationship between these psychological factors and musculoskeletal disorders.
Conclusion: Given the high prevalence of musculoskeletal disorders and the elevated levels of stress, anxiety, and depression among emergency medical personnel—with no significant relationship found between these variables—it is essential to identify other influencing factors in this field. Implementing control measures to improve working conditions and the work environment is critical, especially considering the vital role of emergency medical personnel in the healthcare sector.

Background: Risk-based maintenance (RBM) establishes a framework for optimizing maintenance strategies and decision-making processes. This review study presents a bibliometric analysis of peer-reviewed research in risk-based maintenance literature over recent decades. The main objective is to identify the most relevant research and the newest trends in the field, according to the information found in databases.
Methods: This study uses bibliometric methods to examine 486 publications in the Web of Science (WoS) and Scopus databases up to 21 July 2024. With the help of VOSviewer software, bibliographic connections between these publications are visualized.
Results: The analysis reveals a significant increase in publications on this topic in the past decade, with China, the United States, the United Kingdom, Japan, and Germany being the leading contributors. A large portion of the relevant documents comes from articles in the Journal of Loss Prevention in the Process Industries, with conferences being the most popular medium for sharing industry knowledge. Furthermore, the co-citation network of references reveals seven clusters: Risk Assessment, Risk-Based Maintenance, Decision Making, Inspection, Reliability and Failure Analysis, Maintenance Strategies, and Predictive Maintenance. Each cluster emphasizes strategic planning and the role of risk management in optimizing maintenance processes. Four visual outputs generated by VOSviewer—network map, overlay map, density map, and co-citation network—illustrate the relationships between scholarly articles and the interconnectedness of influential works.
Conclusion: These visualizations highlight the multifaceted nature of RBM research and provide a framework for future investigations aimed at enhancing maintenance strategies across various industries. This analysis serves as a foundational resource for researchers and practitioners seeking to navigate the landscape of RBM, guiding future research directions and practical applications.