Skip to main content

A Bayesian network based study on determining the relationship between job stress and safety climate factors in occurrence of accidents

Abstract

Background

Job stress and safety climate have been recognized as two crucial factors that can increase the risk of occupational accidents. This study was performed to determine the relationship between job stress and safety climate factors in the occurrence of accidents using the Bayesian network model.

Methods

This cross-sectional study was performed on 1530 male workers of Asaluyeh petrochemical company in Iran. The participants were asked to complete the questionnaires, including demographical information and accident history questionnaire, NIOSH generic job stress questionnaire, and Nordic safety climate questionnaire. Also, work experience and the accident history data were inquired from the petrochemical health unit. Finally, the relationships between the variables were investigated using the Bayesian network model.

Results

A high job stress condition could decrease the high safety climate from 53 to 37% and increase the accident occurrence from 72 to 94%. Moreover, a low safety climate condition could increase the accident occurrence from 72 to 93%. Also, the concurrent high job stress and low safety climate could raise the accident occurrence from 72 to 93%. Among the associations between the job stress factor and safety climate dimensions, the job stress and worker’s safety priority and risk non-acceptance (0.19) had the highest mean influence value.

Conclusion

The adverse effect of high job stress conditions on accident occurrence is twofold. It can directly increase the accident occurrence probability and in another way, it can indirectly increase the accident occurrence probability by causing the safety climate to go to a lower level.

Peer Review reports

Background

The International Labor organization describes workplace accidents as unplanned and unpredictable events that create obvious injuries [1]. Based on the statistics, over 264 million occupational accidents and 350,000 mortalities due to those occur every year in the world [2]. The financial burden of occupational accidents and diseases in compared to that of costly Diseases such as cancers, Alzheimer, human immunodeficiency virus (HIV), and cardiovascular illnesses is higher [3]. Different studies have been investigated the causes of accidents. Some of these agents included individual, organizational, environmental, social, job factors [4]. Of effective factors, job stress and safety climate play important roles in the occurrence of accidents.

Job stress, as one of the most common occupational problems, seriously threatens staff health in the world. The results of studies show that higher levels of job stress can directly affect the body’s immune system and mental health. This stress also causes disorders and illnesses such as depression and anxiety, immune deficiency disorders, headaches, musculoskeletal pain, and cardiovascular disease [5, 6]. Furthermore, it can affect individual and organizational performances [7]. Based on the results of studies, occupational stress affects productivity, absenteeism, burnout, and job turnover [8, 9]. There are various definitions of job stress in the literature. McGrath and Altman state that stress is the fundamental imbalance between an individual’s capability and job demand that leads to severe consequences [10]. There are different occupational factors affecting job stress, such as job control, conflict at work, job satisfaction, mental demand, physical environment, social support, and workload and responsibility [11]. If there are no coordination among job requirements, abilities, and desires, then stress can lead to many behavioral, physical, and psychological consequences. The International Labor Organization estimates the costs of job stress to be 1 to 3.5% of the Gross Domestic Product (GDP) of each country [12]. The study results in the developed countries indicate that nearly 30% of the staff suffer from job stress [13]. In addition to the mental and functional consequences, job stress can lead to unsafe behaviors and accidents through impaired focus and decision-making. The results of several studies have proven that stress plays a vital role in 37% of industrial accidents & injuries [13].

Safety climate is another critical factor affecting staff health and accident occurrence. Safety climate defines the workers’ perceptions of the organizational prioritization related to workplace safety compared to business demands [14,15,16]. Accordingly, safety climate has various attributes such as management’s commitment to safety and safety communication, encouraging the workers to the safety behavior in order to promote safety and performance outcomes [16, 17]. The results of several meta-analysis studies show that the safety climate affects safety outcomes such as accidents and injuries, as well as safety compliance and participation behaviors [18, 19]. The results of a review study performed by Kalteh et al. also showed that increasing the level of safety climate can reduce accident incidence rate and improve safety performance [20]. The planned behavior theory describes the role of the safety climate in causing safety behavior [21]. Based on this theory, three central components, including attitude toward the behavior, subjective norms, and perceived behavioral control, are effective in creating unsafe behavior. Fogarty and Shaw demonstrated that the management attitude and safety climate significantly affect these three components and regulate the behavior [21]. Moreover, the safety climate can make the accident occurrence through other paths such as job stress, which can affect job satisfaction, staff engagement, and turnover [22]. Dollard et al. stated that a psychosocial safety climate adjusts the job demand – resource interaction and controls job stress [23]. However, there may also be a reverse path from work stress toward a safe climate. In other words, job stress factors may affect the safety climate and lead to accidents through this path. This assumption can be examined using the Bayesian network model. This type of model has been exploited to analyze the health and safety issues in previous studies. Mohammadfam et al. applied the Bayesian network model for modeling and evaluating the safety behavior of the workers in the industry [24]. Mirzaei et al. represented a novel procedure for risk modeling of a hydrogen gasholder using the Fuzzy Bayesian Network [25]. Herrero et al. used the Bayesian network models for analyzing the effect of working conditions and psychological/physical symptoms on occupational accidents [26]. Ahn et al. exploited the Bayesian network model for diagnosing the relations between work-related musculoskeletal disorders and working characteristics [27]. In the present study, it was aimed to determine the relationship between job stress and safety climate dimensions in the occurrence of accidents using the Bayesian network model.

Material and methods

Figure 1 shows the workflow of the method used in this paper. The total steps of this study included preparing questionnaires, collecting data, and data analysis. The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations of Helsinki.

Fig. 1
figure 1

The workflow of the method used in this paper

Participants

This cross-sectional study was performed on 1530 male workers of Asaluyeh petrochemical company in Iran, from August 2018 to May 2019. The study population consists of the staff employed in various departments, including technical, electrical, machinery, maintenance, mechanical, welding, turning, and supervision units of the petrochemical company. Firstly, a list of people working in each department was created, and subjects were randomly selected from the list. All participants declared their informed consent, using the consent form provided by the medical ethics committee of Tehran University of medical sciences. Before the beginning of the study, the test stages were clearly explained to the participants. Inclusion criteria were having work experience for more than one year and being literate. Exclusion criteria were lack of cooperation and lack of focus on completing the questionnaires.

Data acquisition

Before the beginning of the study, the goals and stages of the study were clearly explained to the participants. Then, they were given the necessary training on how to complete the questionnaires. The questionnaires were completed in the presence of the researchers at the rest times. The questionnaires included demographical information, accident history questionnaire, NIOSH Generic Job Stress Questionnaire (GJSQ), and Nordic Safety Climate Questionnaire (NOSACQ-50). Also, work experience and the accident history data were inquired from the petrochemical health unit.

Data collection instruments

Demographic data and accident history questionnaire

The demographic data including age, education, marital status, job type, working department, working experience, body mass index, and smoking were collected using a researcher-made questionnaire. Also, participants were questioned about their accident experience and accident type at workplaces during the past year.

NIOSH generic job stress questionnaire (GJSQ)

NIOSH Generic Job Stress Questionnaire (GJSQ) developed by the US National Institute for Occupational Safety and Health (NIOSH) was used to measure the different job stress factors among workers. Kazronian et al. [11] have evaluated and validated this questionnaire in Iranian firefighters. They claimed that the Cronbach Alpha Coefficient of this questionnaire is more than 0.7, and its Intra-Cluster Correlation Coefficient is 0.7. However, in the present study, the Cronbach Alpha Coefficient of the questionnaire and the Intra-Class Correlation Coefficient were calculated again. NIOSH Generic Job Stress Questionnaire (GJSQ) encompasses 21 parts or factors; including background information (7 questions), conflict at work (16 questions), employment opportunities (4 questions), general health (17 questions), health conditions (24 questions), feelings about his-self (10 questions), general job information (12 questions), job requirements (10 questions), job satisfaction (4 questions), mental demands (5 questions), non-work activities (7 questions), other health information (33 questions), physical environment (10 questions), problems at work (6 questions), social support (12 questions), work hazards (5 questions), work limitations (5 questions), workload and responsibility (11 questions), job (14 questions), and job future (5 questions). The answer options are different, including yes or no, false and true, Likert from one to five, Likert from one to three, and open-ended answers [28]. The total score was calculated with the mean value of the scores in job stress scales.

Nordic safety climate questionnaire (NOSACQ-50)

It is a validated questionnaire on safety climate survey. A team of occupational safety experts from several Nordic countries including Denmark, Norway, Iceland, Finland, and Sweden led by Pete Kines from the National Research Centre for the Working Environment (NRCWE) developed the questionnaire in 2011 [29]. This questionnaire has 50 questions and 7 subscales; including management’s safety priority and ability (9 questions), management’s safety empowerment (7 questions), management’s safety justice (6 questions), worker’s safety commitment (6 questions), worker’s safety priority and non-acceptance risk (7 questions), peer safety communication, learning, and trust (8 questions), and worker’s trust in the efficacy of safety systems (7 questions) [29]. Yousefi et al. [30] in 2016, have evaluated the validity and reliability of this questionnaire in Iran. Alpha Cronbach Coefficient of this questionnaire obtained was 0.94. However, in the present study, Cronbach Alpha Coefficient and the Intra-Class Correlation Coefficient were calculated again. In this questionnaire, the questions are answered via Likert ranging from one to four; including strongly disagree, disagree, agree, and strongly agree. The total score was calculated with the mean value of the scores in job stress scales.

Data analysis

Statistical tests

First, we discretized the any of understudy variables (the safety climate dimensions and occupational stress scales) with the Uniform-Width method into a dichotomous variable (low and high). Then, the statistical tests were carried out using SPSS software version 24. Descriptive statistics were calculated. Then the expectation-maximization method was applied to calculate and replace the missing values. Cronbach Alpha Coefficient and Internal Correlation Coefficient calculated by a two-way mixed analysis of variance. The result of this analysis was used for evaluating the validity and reliability of the questionnaires. Besides, the bivariate and multivariate correlations coefficients for the job stress and safety climate variables were calculated.

Bayesian network modeling

Bayesian Networks (BNs) are probabilistic and graphical models introduced by Pearl. In the present study, GeNIe academic software version 2.3 (university of Pittsburg) with an expert-knowledge base method was used. Five experts were asked to give their opinion on three possible relationships (A↑B, A → B, and A ← B) and for this purpose, the Dempster-Shafer theory was applied [31]. After creating the BN graphical structure, a Conditional Probability Table (CPT) was obtained from the developed model with the Expectation-Maximization algorithm [32]. In the next step, delta p sensitivity analysis was used to rank the factors corresponding to accident state [24], and finally,10-fold cross-validation analysis was also used to evaluate the model. Dataset was randomly divided into ten equal folds, and in any 10-repeat cross-validation, nine folds (9 subsamples) were used to train the Bayesian network model, and the remaining fold (1 subsample) was used to validate the model. A sensitivity analysis also was performed to investigate the effects of the statistically significant variables [33].

Results

According to the analysis of the collected data, the internal consistency of all dimensions is at an excellent level (Cronbach’s alpha> 0.9). Also, based on the results of the two-way mixed model, the intra-class correlation coefficients for the reliability of a single rating, and the aggregated mean have excellent values. Table 1 shows the descriptive information of the participants’ demographic variables. Most of the subjects were 30 to 39 years old (50.7%), at high school education level (44.9%), 5 to 10 years of job experience (47.3%), with body mass index higher than 25 (54.8%), repairing and machinery job type (36.2%), married (84.4%), and smoker (58.2%). Table 2 also represents the Conditional Probability Table (CPT) for accident state, which reports the relationship coefficient among the variables.

Table 1 The descriptive information of the participants’ demographic variable
Table 2 CPT for “accident state”

Figure 2 shows the Bayesian network model representing the dependencies among the variables the marginal probabilities of the studied variables. Figures 3, 4, 5, 6 also indicate the sensitivity analyses. At the occurrence of the accident with the probability of 100%, the probability of the high job stress enhanced from 50 to 65%, and the probability of the high safety climate decreased from 53 to 40%. For safety climate dimensions, most decreases were related to the management’s safety justice from 53 to 47% and the worker’s safety commitment from 43 to 37%. For the high job stress condition with the probability of 100%, the probability of the high safety climate decreased from 53 to 37% and the probability of the accident occurrence increased from 72 to 94%. For safety climate dimensions, most decreases were related to the worker’s safety priority, and risk non-acceptance from 41 to 25% and the worker’s safety commitment from 43 to 28%. For the low safety climate condition with the probability of 100%, the probability of the accident occurrence increased from 72 to 93%. For the safety climate dimensions, most decreases were related to the management’s safety justice from 53 to 34%. Besides, at the concurrently low safety climate and high job stress with the probability of 100%, the probability of accident occurrence increased from 72 to 93%. Lastly, for the safety climate dimensions, most decreases were also related to the management’s safety justice from 53 to 28%.

Fig. 2
figure 2

Bayesian network model representing the dependencies among the variables the marginal probabilities of the studied variables

Fig. 3
figure 3

Sensitivity analysis of accident occurrence

Fig. 4
figure 4

Sensitivity analysis of job stress

Fig. 5
figure 5

Sensitivity analysis on safety climate

Fig. 6
figure 6

Sensitivity analysis on simultaneously safety climate and job stress

Table 3 demonstrated ranked posterior probabilities of occurring accidents. As Table 4 shows, the calculated values are from the associations between factors. The most effective mean values were related to the job stress and accident occurrence association (0.39), the safety climate and accident occurrence relation (0.34), and the management’s safety justice and safety climate (0.19). For the association between the job stress factor and safety climate dimensions, the job stress and worker’s safety priority and risk non-acceptance relation (0.19) had the highest mean value. For the job stress factor and job stress scales associations, the associations of the job stress factor with general health (0.004), employment opportunity (0.004), and other health information (0.003) showed the highest mean values.

Table 3 Predicted probability of “occupational accident = no” (%) corresponded to other factors states
Table 4 The calculated influence values from the association of the factors in developed model

A ROC curve was drawn to investigate the validity of the fitted Bayesian model, and the area under the curve was calculated (Fig. 7). This value was 0.8353. The parameters of the confusion matrix related to the classification of the occupational accident status also were computed (Table 5). The values of the sensitivity, specificity, and accuracy of the model were equal to 87, 82, and 85%, respectively.

Fig. 7
figure 7

The ROC curve

Table 5 The confusion matrix related to the classification of the occupational accident status

Discussion

In the present study, a Bayesian model was fitted to analyze the inter-association between job stress, safety climate, and accident occurrence. This model has been used to clarify how job stress can affect accident occurrence, directly and indirectly, through the safety climate. The results showed that the presented model has good validity. The validity and reliability of the used questionnaires were investigated and were accepted as valid and reliable tools. In general, the results obtained from the model showed that a high job stress condition could decrease the high safety climate from 53 to 37% and increase the accident occurrence from 72 to 94%. Analysis of the other paths in the model also demonstrated that a low safety climate condition could increase the accident occurrence from 72 to 93%. Also, the concurrent high job stress and low safety climate could raise the accident occurrence from 72 to 93%. These results show that two factors of job stress and safety climate play an essential role in the accident occurrence.

Job stress has a significant impact on the occurrence of accidents both directly and indirectly through decreasing the safety climate. Several studies have indicated this path as a direct impact. Barkhordari et al. represented a model that occupational stress can lead to individual failure and carelessness, which can increase unsafe acts or conditions and thereby cause accidents and injuries [34]. The high job stress and imbalance between individual capability and job demand reduce the person’s cognitive performance and create cognitive failure. Therefore, individuals cannot decide and act correctly [35]. Chou et al. concluded that perceived occupational stress decreases the cortical activity of the prefrontal cortex [36]. According to Mohammadfam et al., there is a significant direct association between job stress and unsafe acts and accidents. Therefore, the increase of one score in occupational stress enhances the unsafe acts by 1 % [37]. Julia et al. also investigated the relationship between occupational stress and accident occurrence and concluded that there is a significant positive association between them [38]. The results of the present study are compatible with the stated studies. The indirect effect path of the job stress on the accident occurrence was through the effect on the safety climate.

Occupational stress appears to affect the safety climate through two probable significant paths. Job stress can cause physical and psychological consequences. Chronic stress and adverse working conditions cab be resulted in job burnout and cause physical and psychological consequences. The physical effects include hypercholesterolemia, type 2 diabetes, coronary heart disease, hospitalization due to cardiovascular disorder, musculoskeletal pain, changes in pain experiences, prolonged fatigue, headaches, gastrointestinal issues, and respiratory problems. The psychological effects consisted of insomnia, depressive symptoms, use of psychotropic and antidepressant medications, hospitalization for mental disorders, and psychological ill-health symptoms. Job dissatisfaction, absenteeism, new disability pension, job demands, job resources, and presenteeism [39]. The results of a study performed by McCaughey et al. showed that workplace injuries and illnesses had negative effects on perceptions of safety climate [40]. On the other hand, job stress may provoke negative feelings in the workplace [41]. These feelings can impress on the job satisfaction of the workers. Stoilkovska et al. concluded that job satisfaction had a significant effect on the perceived safety climate [42].

The results of the present study showed that at the concurrent high job stress and low safety climate situation, the management’s safety justice has the most decrease from 53 to 28% among the safety climate dimensions. On the other hand, the perception of the health problem creates job stress and affects the safety climate. In the current study, however, the relationships between the job stress factor and job stress scales, the relations of the job stress factor with general health, work hazard, and other health information showed the highest mean influence values. McCaughey et al. demonstrated that workplace injury and illness have adverse effects on the safety climate perceptions and health care worker outcomes [40]. Hall et al. also concluded that the safety climate modulates the impact of the job demand on depression and further the effect of the depression on the positive organizational behaviors [43].

Also, the results of the present study indicated that at the high job stress condition, most decreases were related to the worker’s safety priority and risk non-acceptance, and the worker’s safety commitment. Among the associations between the job stress factor and safety climate dimensions, the job stress and worker’s safety priority and risk non-acceptance had the highest mean influence value. The results of Griffin et al. study revealed that job stress has a significant positive association with depersonalization and a negative relationship with organizational commitment [44]. Bronkhorst also stated that the job demands, job resources, and safety climate affect the physical and psychological safety behavior and the safety climate has the mediator role in the association between job demands and job resources with the safety behaviors [45]. These results are compatible with the results of the present study.

Limitations of the present study included the lack of data analysis in different parts of the industry and job positions. Also, the effect of non-occupational stress due to family and community environments was not investigated, and the effect of demographic variables on the presented model was not studied. Therefore, it recommends that these limitations be solved in future studies. Besides, the effect of the proposed model on the number and severity of unsafe behaviors and the number and severity of work accidents were not considered, which authors will investigate it in subsequent studies. Lastly, this study applies the self-report instruments as the data source, which may cause some bias in the results.

Conclusions

In this paper, we utilize the flexible modeling and inference capability BN to perform a study to investigate the effects of job stress and safety climate on the occurrence of an accident. The results of this study showed that the adverse effect of high job stress conditions on accident occurrence is twofold. It can directly increase the accident occurrence probability and in another way, it can indirectly increase the accident occurrence probability by causing the safety climate to go to a lower level. Therefore, to improve the perceived safety climate and decrease the accident occurrence probability, the organizations should put more effort to reduce the job stress on the employee. Some of the strategies for handling the stress in the workplaces included providing a balance between demands and resources, increasing social supports, increasing job control, decreasing role pressures, increasing job satisfaction, increasing job security, and strengthening problem-oriented coping style.

Availability of data and materials

All data generated and analyzed during this step of the study are included in this published article.

Abbreviations

GDP:

Gross Domestic Product

GJSQ:

Generic Job Stress Questionnaire

NOSACQ-50:

Nordic Safety Climate Questionnaire

NRCWE:

National Research Centre for the Working Environment

BN:

Bayesian Networks

CPT:

Conditional Probability Table

References

  1. Gulhan B, Ilhan M, Civil E. Occupational accidents and affecting factors of metal industry in a factory in Ankara. Türkiye Halk Sağlığı Dergisi. 2012;10(2):76–85.

    Article  Google Scholar 

  2. Hämäläinen P. The effect of globalization on occupational accidents. Saf Sci. 2009;47(6):733–42.

    Article  Google Scholar 

  3. Cohen MA, Clark RE, Silverstein B, Sjostrom T, Spielholz P. Work-related deaths in Washington state, 1998–2002. J Saf Res. 2006;37(3):307–19.

    Article  Google Scholar 

  4. Khoshakhlagh AH, Yazdanirad S, Laal F, Sarsangi V. The relationship between illnesses and medical drug consumption with the occurrence of traffic accidents among truck and bus drivers in Tehran, Iran. Chin J Traumatol. 2019;22(3):142–7.

    Article  Google Scholar 

  5. Blackmore ER, Stansfeld SA, Weller I, Munce S, Zagorski BM, Stewart DE. Major depressive episodes and work stress: results from a national population survey. Am J Public Health. 2007;97(11):2088–93.

    Article  Google Scholar 

  6. Lotfi S, Yazdanirad S, Pourabdiyan S, Hassanzadeh A, Lotfi A. Driving behavior among different groups of Iranian drivers based on driver coping styles. Int J Prev Med. 2017;8.

  7. Pourabdian S, Lotfi S, Yazdanirad S, Golshiri P, Hassanzadeh A. Evaluation of the effect of fatigue on the coping behavior of international truck drivers. BMC psychology 2020;8(1):1-0.

  8. Yazdanirad S, Sadeghian M, Naeini MJ, Abbasi M, Mousavi SM. The contribution of hypochondria resulting from Corona virus on the occupational productivity loss through increased job stress and decreased resilience in the central workshop of an oil refinery: a path analysis. Heliyon. 2021;7(4):e06808.

    Article  Google Scholar 

  9. Vagg PR, Spielberger CD, Wasala CF. Effects of organizational level and gender on stress in the workplace. Int J Stress Manag. 2002;9(4):243–61.

    Article  Google Scholar 

  10. McGrath JE. Social and psychological factors in stress: Illinois Univ at Urbana Dept of Psychology; 1970.

    Google Scholar 

  11. Kazronian S, Zakerian S, Saraji J, Hosseini M. Reliability and validity study of the NIOSH generic job stress questionnaire (GJSQ) among firefighters in Tehran city. Health Safety Work. 2013;3(3):25–34.

    Google Scholar 

  12. Cassidy T. Stress, cognition and health: psychology press; 1999.

    Google Scholar 

  13. M. Goldenhar L, Williams LJ, G. Swanson N. Modelling relationships between job stressors and injury and near-miss outcomes for construction labourers. Work Stress 2003;17(3):218-240.

  14. Griffin MA, Neal A. Perceptions of safety at work: a framework for linking safety climate to safety performance, knowledge, and motivation. J Occup Health Psychol. 2000;5(3):347.

    Article  CAS  Google Scholar 

  15. Zohar D. A group-level model of safety climate: testing the effect of group climate on microaccidents in manufacturing jobs. J Appl Psychol. 2000;85(4):587.

    Article  CAS  Google Scholar 

  16. Griffin MA, Curcuruto M. Safety climate in organizations. Annual Review of Organizational Psychology and Organizational Behavior. 2016;3:191–212.

    Article  Google Scholar 

  17. Casey T, Griffin MA, Flatau Harrison H, Neal A. Safety climate and culture: integrating psychological and systems perspectives. J Occup Health Psychol. 2017;22(3):341.

    Article  Google Scholar 

  18. Beus JM, Payne SC, Bergman ME, Arthur W Jr. Safety climate and injuries: an examination of theoretical and empirical relationships. J Appl Psychol. 2010;95(4):713.

    Article  Google Scholar 

  19. Nahrgang JD, Morgeson FP, Hofmann DA. Safety at work: a meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes. J Appl Psychol. 2011;96(1):71.

    Article  Google Scholar 

  20. Kalteh HO, Mortazavi SB, Mohammadi E, Salesi M. The relationship between safety culture and safety climate and safety performance: a systematic review. Int J Occup Saf Ergon. 2021;27(1):206–16.

    Article  Google Scholar 

  21. Fogarty GJ, Shaw A. Safety climate and the theory of planned behavior: towards the prediction of unsafe behavior. Accid Anal Prev. 2010;42(5):1455–9.

    Article  Google Scholar 

  22. Huang Y-H, Lee J, McFadden AC, Murphy LA, Robertson MM, Cheung JH, et al. Beyond safety outcomes: an investigation of the impact of safety climate on job satisfaction, employee engagement and turnover using social exchange theory as the theoretical framework. Appl Ergon. 2016;55:248–57.

    Article  Google Scholar 

  23. Dollard MF, Tuckey MR, Dormann C. Psychosocial safety climate moderates the job demand–resource interaction in predicting workgroup distress. Accid Anal Prev. 2012;45:694–704.

    Article  Google Scholar 

  24. Mohammadfam I, Ghasemi F, Kalatpour O, Moghimbeigi A. Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Appl Ergon. 2017;58:35–47.

    Article  Google Scholar 

  25. Aliabadi MM, Pourhasan A, Mohammadfam I. Risk modelling of a hydrogen gasholder using fuzzy Bayesian network (FBN). Int J Hydrog Energy. 2020;45(1):1177–86.

    Article  Google Scholar 

  26. García-Herrero S, Mariscal MA, García-Rodríguez J, Ritzel DO. Working conditions, psychological/physical symptoms and occupational accidents. Bayesian network models. Saf Sci. 2012;50(9):1760–74.

    Article  Google Scholar 

  27. Ahn G, Hur S, Jung MC. Bayesian network model to diagnose WMSDs with working characteristics. Int J Occup Saf Ergon. 2018.

  28. Çetinkaya G, Dicle A. Work stress, perception of job safety, and job satisfaction of rope access technicians and the relationship among them İple Erişim Teknisyenlerinin İş Stresi, İş Güvenliği Algıları, İş Tatminleri ve Aralarındaki İlişki Üzerine bir Çalışma. Work Stress. 2017;7(2):125–32.

    Google Scholar 

  29. Kines P, Lappalainen J, Mikkelsen KL, Olsen E, Pousette A, Tharaldsen J, et al. Nordic Safety Climate Questionnaire (NOSACQ-50): A new tool for diagnosing occupational safety climate. International Journal of industrial Ergonomics. 2011;41(6):634-46.

  30. Yousefi Y, Jahangiri M, Choobineh A, Tabatabaei H, Keshavarzi S, Shams A, et al. Validity assessment of the Persian version of the Nordic safety climate questionnaire (NOSACQ-50): a case study in a steel company. Saf Health Work. 2016;7(4):326–30.

    Article  Google Scholar 

  31. Sentz K, Ferson S. Combination of evidence in Dempster-Shafer theory: Sandia National Laboratories Albuquerque; 2002.

    Book  Google Scholar 

  32. Liu X, Huang G, Huang H, Wang S, Xiao Y, Chen W. Safety climate, safety behavior, and worker injuries in the Chinese manufacturing industry. Saf Sci. 2015;78:173–8.

    Article  Google Scholar 

  33. Cao Y, Fang X, Ottosson J, Näslund E, Stenberg E. A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery. J Clin Med. 2019;8(5):668.

    Article  Google Scholar 

  34. Barkhordari A, Malmir B, Malakoutikhah M. An analysis of individual and social factors affecting occupational accidents. Saf Health Work. 2019;10(2):205–12.

    Article  Google Scholar 

  35. Day AJ, Brasher K, Bridger RS. Accident proneness revisited: the role of psychological stress and cognitive failure. Accid Anal Prev. 2012;49:532–5.

    Article  Google Scholar 

  36. Chou P-H, Lin W-H, Hung C-A, Chang C-C, Li W-R, Lan T-H, et al. Perceived occupational stress is associated with decreased cortical activity of the prefrontal cortex: a multichannel near-infrared spectroscopy study. Sci Rep. 2016;6:39089.

    Article  CAS  Google Scholar 

  37. Mohammadfam I, Bahrami A, Fatemi F, Golmohammadi R, Mahjub H. Evaluation of the relationship between job stress and unsafe acts with occupational accidents in a vehicle manufacturing plant. Avicenna Journal of Clinical Medicine. 2008;15(3):60–6.

    Google Scholar 

  38. Julià M, Catalina-Romero C, Calvo-Bonacho E, Benavides FG. The impact of job stress due to the lack of organisational support on occupational injury. Occup Environ Med. 2013;70(9):623–9.

    Article  Google Scholar 

  39. Salvagioni DA, Melanda FN, Mesas AE, González AD, Gabani FL, Andrade SM. Physical, psychological and occupational consequences of job burnout: a systematic review of prospective studies. PLoS One. 2017;12(10):e0185781.

    Article  Google Scholar 

  40. McCaughey D, DelliFraine JL, McGhan G, Bruning NS. The negative effects of workplace injury and illness on workplace safety climate perceptions and health care worker outcomes. Saf Sci. 2013;51(1):138–47.

    Article  Google Scholar 

  41. Kawano Y. Association of job-related Stress Factors with psychological and somatic symptoms among Japanese hospital nurses: effect of departmental environment in acute care hospitals. J Occup Health. 2008;50(1):79–85.

    Article  Google Scholar 

  42. Stoilkovska BB, Žileska Pančovska V, Mijoski G. Relationship of safety climate perceptions and job satisfaction among employees in the construction industry: the moderating role of age. Int J Occup Saf Ergon. 2015;21(4):440–7.

    Article  Google Scholar 

  43. Hall GB, Dollard MF, Winefield AH, Dormann C, Bakker AB. Psychosocial safety climate buffers effects of job demands on depression and positive organizational behaviors. Anxiety, Stress & Coping. 2013;26(4):355–77.

    Article  Google Scholar 

  44. Griffin ML, Hogan NL, Lambert EG, Tucker-Gail KA, Baker DN. Job involvement, job stress, job satisfaction, and organizational commitment and the burnout of correctional staff. Crim Justice Behav. 2010;37(2):239–55.

    Article  Google Scholar 

  45. Bronkhorst B. Behaving safely under pressure: the effects of job demands, resources, and safety climate on employee physical and psychosocial safety behavior. J Saf Res. 2015;55:63–72.

    Article  Google Scholar 

Download references

Acknowledgements

Researchers need to thank all staff who have participated in this study.

Funding

This study has been supported by Tehran University of Medical Sciences, project number 97-03 -61-38355. The funding body has played no role in the design of the study, collection, and analysis of data, interpreting results or in writing the manuscript or in decision to submit the manuscript for publication. The content of this publication is only the responsibility of the authors.

Author information

Authors and Affiliations

Authors

Contributions

AHK collected the data and contributed to the manuscript. SY designed the study and wrote the manuscript. MMK and EK were involved in the interpretation of the data, and the coordinator researcher. YH and SK carried out the statistical analyses of the data. All authors read and proved the final manuscript.

Corresponding author

Correspondence to Saeid Yazdanirad.

Ethics declarations

Declaration of Helsinki

The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations.

Ethics approval and consent to participate

The protocol was reviewed and approved by the Medical Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1398.558). All steps of the study were in accordance with the ethical standards. All participants were asked to fill out the consent form developed by the medical ethics committee, and written informed consent was obtained from all of them.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khoshakhlagh, A.H., Yazdanirad, S., Kashani, M.M. et al. A Bayesian network based study on determining the relationship between job stress and safety climate factors in occurrence of accidents. BMC Public Health 21, 2222 (2021). https://doi.org/10.1186/s12889-021-12298-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-021-12298-z

Keywords