Suchergebnisse

The prevalence of fire in laboratories is high due to the potential sources of the fire hazards, including the use of various equipment and chemicals, as well as the possibility of human error. Accordingly, this study was designed and conducted with the aim of developing a tool to identify hazards and assess laboratory fire risk. In the present study, we used the Delphi technique in three rounds in 2020 with the participation of 17 experts in the fields of safety engineering, occupational health engineering, chemistry, and chemical engineering. The scattering index of coefficient of variation (CV) was higher than 20% and the acceptance criterion for each item was considered with an average of ≥4. In addition, the validity and reliability of this tool were evaluated using content validity ratio (CVR), content validity index (CVI), and Cronbach's alpha reliability index. After three rounds of Delphi study, the hazard identification and risk assessment questionnaire for laboratory fire were developed with 45 items, including chemical materials and compounds, electricity, and fire response. The coefficient of variation (CV) in this Delphi study was equal to 0.05. The results of the validity and reliability evaluation of this tool showed that the content validity index (CVI) and Cronbach's alpha coefficient were equal to 0.902 and 0.88. The questionnaire of the present study indicated an acceptable validity and reliability for identifying hazards and assessing laboratory fire risk. Therefore, this tool provided a quick, easy, and practical way to identify hazards and assess the risk of laboratory fires.

Enhancing the index of crisis resilience is one of the key goals in medical environments. Various parameters can affect crisis resilience. The current study was designed to analyze crisis resilience in medical environments based on the crisis management components. This cross-sectional and descriptive-analytical study was performed in 14 hospitals and medical centers, in 2020. A sample size of 343.5 was determined based on the Cochran's formula. We used a 44-item crisis management questionnaire of Azadian et al. to collect data. The components of this questionnaire included management commitment, error learning, culture learning, awareness, preparedness, flexibility, and transparency. The data was analyzed based on the structural equation modeling approach using IBM SPSS AMOS v. 23.0. The participants' age and work experience mean were 37.78±8.14 and 8.22±4.47 years. The index of crisis resilience was equal to 2.96±0.87. The results showed that all components of crisis management had a significant relationship with this index (p <0.05). The highest and lowest impact on the resilience index were related to preparedness (E=0.88) and transparency (E=0.60). The goodness of fit indices of this model including RMSEA, CFI, NFI, and NNFI (TLI) was 2.86, 0.071, 0.965, 0.972, and 0.978. The index of crisis resilience in the medical environments was at a moderate level. Furthermore, the structural equation modeling findings indicated that the impact of each component of crisis management should be considered in prioritizing measures to increase the level of resilience.