Relationships between job stress, post-traumatic stress and musculoskeletal symptoms in firefighters and the role of job burnout and depression mediators: a bayesian network model.

INTRODUCTION: Job stress, post-traumatic stress disorder (PTSD), and negative psychological outcomes in firefighters can be caused, or aggravated, by their work. These mental disorders can impart musculoskeletal symptoms. This study aimed to investigate relationships between musculoskeletal and psychological disorders in a population of firefighters using a Bayesian network model. METHODS: This cross-sectional study, conducted in 2022, included 2339 firefighters who completed questionnaires during their rest periods. The questionnaires comprised of demographical information, the Occupational Stress Questionnaire-HSE, the PTSD Checklist, Maslach Burnout Inventory, Center for Epidemiologic Studies - Depression scale (CES-D), and Nordic Musculoskeletal Questionnaire. GeNIe academic software was used to analyze the Bayesian network. RESULTS: High job stress and high PTSD each increased the probability of musculoskeletal symptoms by 34%. When combined, high job stress and high PTSD increased the probability of musculoskeletal symptoms by 37%. Among the mediator's burnout and depression, depression had the highest association with musculoskeletal symptoms. CONCLUSIONS: Job stress and PTSD can increase musculoskeletal symptoms and are influenced by psychological mediators (like burnout and depression). Adopting preventive and therapeutic measures to mitigate job stress and PTSD, mitigate and rehabilitate WMSD, and manage associated mediators are critical for the mental and physical health of firefighters.

A fuzzy parametric model for decision making involving F-OWA operator with unknown weights environment.

Weight determining of attributes is an important factor in decision support systems since it corresponds to the relative importance of each criteria which is necessary to be determined since all the attributes aren't equally important. The aim of this paper is to put forward a method for multi Criteria decision making (MCDM) problems based on three trapezoidal fuzzy numbers under completely unknown weights environment. Based on the idea that the attribute with a larger deviation value among alternatives should be assigned a larger weight, an optimization model based on maximizing deviation method is established. F-OWA is considered to be vastly superior from the existing operators which usually take into account only the relative significance of decision makers. F-OWA operator considers not only the ratings of attribute values but also their ordered position that is it not only signifies the decision makers but also values the individual assessments. We utilize fuzzy ordered weighted averaging (F-OWA) operator to compute the collective overall preference value of each alternative and select the most desirable one according to their expected score values. The presented method is more generalized since we have used TTFNs, which are more effective in capturing uncertainty than IT2FS, just like triangular fuzzy numbers have a better representational power than simple interval numbers. Moreover, an illustrative example is given for the justification of the proposed technique.

Examining the effect of safety climate on accident risk through job stress: a path analysis.

BACKGROUND: Job stress is a probable mediator of the relationship between safety climate and accident occurrence. To demonstrate this, this study investigates the relationship between safety climate, job stress, and accident risk using a large number of surveys. The study will use structural equation modeling (SEM) to analyze the data collected from the surveys to examine the effect of safety climate on accident risk through job stress. METHODS: The study is a cross-sectional study that was conducted on 1,530 male workers of a petrochemical company. The subjects were asked to complete several questionnaires during rest periods, which included demographic information, the Nordic safety climate questionnaire (NOSACQ-50), and the generic job stress questionnaire (GJSQ). Additionally, data on the frequency and intensity of accidents among participants were gathered from the health unit of the company. Path analysis was conducted by structural equation modeling (SEM) in Analysis of Moment Structures (AMOS) software. RESULTS: The results revealed that the latent variable of safety climate with an effect coefficient of - 0.112 did not have a direct effect on accident risk (P = 0.343). However, safety climate with an effect coefficient of - 0.633 had an indirect effect on accident risk through job stress (P < 0.001). The total score of job stress had a significant direct effect (0.649) on accident risk (P < 0.001). Among the dimensions of safety climate, the variables of management's safety priority, commitment, and competence (- 0.108) and workers' safety commitment (- 0.107) had the highest indirect effect coefficients on accident risk. Among the dimensions of job stress, the highest indirect effects belonged to the variables of conflict at work (0.636), physical environment (0.631), and workload and responsibility (0.631), respectively. CONCLUSION: The results of the study revealed that job stress mediates the relationship between safety climate and accident risk. This finding suggests that organizations can potentially decrease accidents in industries by addressing and managing job stress in the workplace.