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

A Bayesian approach to reveal the role of psychological factors on turnover intention among nurses during the COVID-19 pandemic

BACKGROUND

Turnover intention is considered a significant challenge for healthcare and treatment organizations. The challenging conditions of treating COVID-19 patients and the physical and mental stress imposed on nurses during the pandemic may lead them to leave their jobs. The present study aimed to determine the role of psychological factors (general health, mental workload, work-family conflicts, and resilience) on turnover intention using a Bayesian approach during the COVID-19 pandemic.

METHODS

The present cross-sectional study was carried out during the winter of 2021 at three hospitals in Khuzestan Province, Iran. To collect data for this investigation, 300 nurses were chosen based on Cochran's formula and random sampling technique. Seven questionnaires, including General Health, Mental Workload, Work-Family Conflict, Resilience, Job Stress, Fear of COVID-19, and Turnover Intention Questionnaires. Bayesian Networks (BNs) were used to draw probabilistic and graphical models. A sensitivity analysis also was performed to study the effects of the variables. The GeNIe academic software, version 2.3, facilitated the examination of the Bayesian network.

RESULTS

The statistically significant associations occurred between the variables of fear of COVID-19 and job stress (0.313), job stress and turnover intention (0.302), and resilience and job stress (0.298), respectively. Job stress had the highest association with the fear of COVID-19 (0.313), and resilience had the greatest association with the work-family conflict (0.296). Also, the association between turnover intention and job stress (0.302) was higher than the association between this variable and resilience (0.219). At the low resilience and high job stress with the probability of 100%, the turnover intention variable increased by 20%, while at high resilience and low job stress with the probability of 100%, turnover intention was found to decrease by 32%.

CONCLUSION

In general, the results showed that four psychological factors affect job turnover intention. However, the greatest impact was related to job stress and resilience. These results can be used to manage job turnover intention in medical environments, especially in critical situations such as COVID-19.

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.

Providing a comprehensive approach to oil well blowout risk assessment

Drilling is one of the most dangerous activities in the oil and gas process industries. Therefore, a holistic approach was presented to prevent and control risks and reduce the uncertainty of blowouts, kick scenarios, and control layers based on the Fuzzy Bayesian Network (FBN). Four independent protection layers (IPLs) were used to evaluate the blowout outcome, and the failure probabilities of IPL1 and IPL2 were calculated with Fault Tree Analysis (FTA). Then, different events were transferred to the Bayesian Network (BN) in GeNIe software, and updated the probabilities. The Fuzzy Fault Tree (FFT) results reveal that the failure probabilities for IPL1 and IPL2 amount to 8.94×10-4 and 4.97×10-21, respectively. Also, the blowout probability results based on FFT were larger than FBN. According to FBN, the probability of the kick event was equal to 6.60×10-34. Sensitivity analysis showed that X1 (Mud volume/flow change) had the highest degree of importance in the blowout of oil wells. The results of this study can be used in both a preventive and reactive approach. Deductive and inductive reasoning, the dynamic nature and conditional dependencies, and causal relationships between events can make the model more realistic.

Determination and prioritization of factors affecting the occurrence of needle stick injuries among healthcare workers using techniques of Delphi and fuzzy analytical hierarchy process (FAHP)

INTRODUCTION

Needlestick injuries (NSIs) are a major hazard in the workplace for healthcare workers. To prevent these injuries, it is essential to determine the important factors affecting the occurrence of them. This study aimed to identify, classify and prioritize these factors using techniques of Delphi and fuzzy analytical hierarchy process (FAHP).

METHODS

This descriptive-analytical study was conducted in 2022. Firstly, the factors affecting the occurrence of needlestick injuries were identified by the literature review. Moreover, the Delphi technique was used to identify the factors. 20 experts (physicians, nurses, and occupational health experts) participated in the steps of the Delphi method. Then, these factors were grouped into six groups. In the next step, the fuzzy analytical hierarchy process (FAHP) was applied to prioritize the factors. For this purpose, the pairwise comparison questionnaire was designed and filled out by 20 experts. Finally, data were analyzed using MATLAB software (version 2018a).

RESULTS

42 factors (31 factors extracted from the literature review and 11 factors obtained from the Delphi technique) were identified in this study. These factors were categorized into six groups. Based on the results, the relative weight of non-demographic personal factors, tool and technology factors, job factors, organizational factors, demographic personal factors, and environmental factors were computed by 0.200, 0.185, 0.184, 0.157, 0.142, and 0.133, respectively.

CONCLUSION

These results determined the importance of the factors affecting the occurrence of needlestick injuries. These findings can be useful for planning preventive measures.

Prioritizing risk factors of hazardous material road transportation accidents using the fuzzy AHP method

BACKGROUND

Annually, large amounts of hazardous materials (hazmat) are transported through the roads and this movement causes various accidents. Identifying the causes of these accidents is a critical issue for all public governments.

OBJECTIVES

This study aimed to identify the effective risk factors for hazmat road transport accidents and determine their relative weight using the fuzzy analytical hierarchy process (AHP) method.

METHODS

This qualitative study was conducted in 2021 in Iran and included four steps, i.e., the identification (using literature review and semi-structured interview), determination (according to the expert panel opinion), classification, and prioritization of effective factors in hazmat road transportation accidents. To prioritize and determine the relative weight of the effective factors, the fuzzy AHP technique was used.

RESULTS

In total, 159 risk factors were identified, which were classified into six factors (including road, transportation management, vehicle, cargo, driver, and weather conditions) and 24 bub-factors. The main factor (greatest relative weight) with the highest priority was the driver (0.181). The road (0.167), cargo (0.166), vehicle (0.169), transportation management (0.161), and weather conditions (0.159) were the next priorities, in that order.

CONCLUSION

The results demonstrated that the driver is the most important factor in causing accidents when transporting hazmat by road. The findings of this study might have the potential to decrease the frequency and consequence of accidents caused by the road transport of hazmat.

Safety culture in French nursing homes: A randomised controlled study to evaluate the effectiveness of a risk management intervention associated with care

BACKGROUND

French Nursing Homes (NHs) are in the early stages of implementing their Risk Management (RM) approach. A regional structure, which was mandated to provide independent support in RM, designed a training package.

OBJECTIVE

To study the impact of the RM training package on safety culture (SC) in NHs and drivers for improvement in SC scores.

METHOD AND ANALYSIS

This randomised controlled study targeted French NHs. Inclusion criteria were voluntary participation, no external support provided on the topic of adverse incidents upstream of the project, and the commitment of top management to its implementation. The 61 NHs were randomly allocated to one of two groups: the first benefited from a training package; support was given to the second after the impact measurement. Seven dimensions of SC were measured, at an 18-month interval, using the validated Nursing Home Survey on Patient Safety Culture questionnaire (22 items), which was administered to all of the professionals working in NHs. Eleven variables were captured, relating to the structural profile of the NH, the choices of top management in terms of healthcare safety, and the implementation of the system. Further modelling identified predictive factors for changes in SC scores.

RESULTS

95% of NHs completed both rounds of the questionnaire. The dimension Feedback and communication about incidents (SC = 85.4% before the intervention) significantly improved (+2.8%; p = 0.044). Improvement in the dimension Overall perceptions of resident safety-organizational learning was close to significant (+3.1%; p = 0.075). Drivers for improvement in scores were a pre-existing quality improvement approach, and a steering group that showed RM leadership.

CONCLUSIONS

The system appears to have improved several dimensions of SC. Our findings are all the more important given the current crisis in the healthcare sector.

TRIAL REGISTRATION

Retrospectively registered as NCT02908373 (September 21, 2016).