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.

Prioritization of control measures in leakage scenario using Hendershot theory and FBWM-TOPSIS.

Currently, there is increasing concern about the safety and leakage of process industries. Therefore, the present study aims to prioritize control measures before and after the leakage scenario by using the Hendershot theory and MCDM techniques. In this study, two proactive and reactive layers were selected before and after leakage of tanks, respectively. Then, criteria and alternatives were selected to perform fuzzy TOPSIS (FTOPSIS) and find the best alternative based on the literature review and Hendershot approach. The linear model of the fuzzy Best-Worst method (FBWM) was constructed and resolved using Lingo 17 software. Subsequently, criteria were assigned weights based on thorough calculations of the inconsistency rate. The weight of study experts was equal to 0.25. The results of FBWM showed that the reliability index with a weight of 0.3727 was ranked first and the inconsistency rate ([Formula: see text]) was calculated to be equal to 0.040. Inherent Safety Design (ISD) (0.899) and passive safety (0.767) also ranked first before and after tank leaks, respectively. Using the FBWM method leads to fewer pairwise comparisons and at the same time more stability. Although ISD and passive strategies are more valid and strict, elements of all strategies are necessary for a comprehensive process safety management program.

Effect of anthropometric and demographic factors on musculoskeletal disorders in nurses' aides.

BACKGROUND: Nurses' aides usually face various stressors, making them prone to musculoskeletal disorders (MSDs). OBJECTIVE: This study evaluates the effect of ergonomic and anthropometric indices, postural risk, and demographic variables on MSDs in nurses' aides. METHODS: Demographic variables, anthropometric dimensions, postural risk level (RL), and related percentiles of 75 nurses' aides were examined using Quick Exposure Check (QEC) software, caliper, and body map questionnaire. Pearson correlation coefficient, univariate and multivariate tests were used to analyze the data. RESULTS: The results showed that the RL of QEC in both groups of males and females was 73.67±22.34 (RL = 4) and 65.34±18.38 (RL = 3), respectively. The level of MSDs in the lumbar, thigh, and leg areas was higher than in other areas. Also, increasing age and work experience, and BMI were significantly associated with increasing disorders in the hands, wrists, and shoulders, respectively (P < 0.05). The results showed that an increase in some anthropometric indices such as body height, buttock, knee, popliteal height, abdomen depth, standing grip access limit, sitting grip access limit increased disorders in the hands and wrists. Also, MSDs were predicted in different areas of the nurses' aides' bodies using regression models, which was significant in the hands, wrists, elbows, legs, and shoulders (P < 0.05). CONCLUSIONS: The results showed that there was a significant difference between male and female nurses in most aspects of anthropometry and the risk level of QEC. Therefore, it is necessary to pay attention to anthropometric dimensions, and demographic diversity to design tools and workstations.