Academic resilience of nursing students during COVID-19: An analysis using machine learning methods.

AIM: This cross-sectional study investigates the factors that contribute to academic resilience among nursing students during COVID-19 pandemic. DESIGN: A cross-sectional study. METHODS: A survey was conducted in a general hospital between November and December 2022. The Nursing Student Academic Resilience Inventory (NSARI) model was used to assess the academic resilience of 96 nursing students. The Boruta method was then used to identify the core factors influencing overall academic resilience, and rough set analysis was used to analyse the behavioural patterns associated with these factors. RESULTS: Attributes were categorised into three importance levels. Three statistically significant attributes were identified ("I earn my patient's trust by making suitable communication," "I receive support from my instructors," and "I try to endure academic hardship") based on comparison with shadow attributes. The rough set analysis showed nine main behavioural patterns. Random forest, support vector machines, and backpropagation artificial neural networks were used to test the performance of the model, with accuracies ranging from 73.0% to 76.9%. CONCLUSION: Our results provide possible strategies for improving academic resilience and competence of nursing students.

Application of failure mode and effect analysis in ICU admission of potentially COVID-19 infected patients.

BACKGROUND: To analyze the admission and treatment process of potentially COVID-19-infected patients in the intensive care unit under normalization, prevention, and control of the pandemic. METHODS: A multidisciplinary team was assembled to develop a flowchart of potentially COVID-19-infected patients admitted to the intensive care unit and identify potential failure steps and modes throughout the process using the failure mode and effect analysis method. Through risk priority number (RPN) analysis of each failure mode, those with the highest impact on nosocomial infection were identified, and the priority of implementation was determined. Related corrective measures have been developed to continuously improve clinical practice and management. RESULTS: Eighty potential failure modes were identified, and 8 potential failure modes were identified with RPNs greater than 100. These high RPNs of the failure modes were associated with careless inquiries of epidemiological histories by nurses, inadequate implementation of management standards by nursing assistants, and exposure of attending physicians to potentially risky environments. Finally, 18 general corrective measures are proposed. CONCLUSIONS: Application of the failure mode and effect analysis method for quality improvement is a powerful tool for predicting potential failures in the process and can suggest corrective measures that could help avoid nosocomial infection during a pandemic.

Supplier selection to support environmental sustainability: the stratified BWM TOPSIS method.

Organisations need to develop long-term strategies to ensure they incorporate innovation for environmental sustainability (IES) to remain competitive in the market. This can be challenging given the high level of uncertainty regarding the future (e.g., following the COVID pandemic). Supplier selection is an important decision that organisations make and can be designed to support IES. While the literature provides various criteria in the field of IES strategies, it does not identify the criteria which can be utilised to assist organisations in their supplier selection decisions. Moreover, the literature in this field does not consider uncertainty related to the occurrence of possible future events which may influence the importance of these criteria. To address this gap, this paper develops a novel criteria decision framework to assist supplier evaluation in organisations, taking into consideration different events that may occur in the future. The framework that combines three decision-making methods: the stratified multi-criteria decision-making method, best worst method, and technique for order of preference by similarity to ideal solution. The framework, proposed in this paper, can also be adopted to enable effective and sustainable decision making under uncertainty in various fields.

Exploring the mutual influence among the social innovation factors amid the COVID-19 pandemic.

From the triple bottom line, the social aspect has received relatively limited attention during the Corona Virus Disease (COVID-19) pandemic, particularly in the emerging economies. Social innovation factors help improve the sustainability performance of the companies. This study develops a social innovation decision framework and analyses the interrelationships among social innovation factors considering the COVID-19 situation. For this purpose, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) is extended by integrating the Z numbers and rough fuzzy set theory into its computational procedure. Z-numbers address the uncertainty of the decision and experts' confidence in the evaluation and rough numbers are used for aggregating the experts' opinions. On this basis, the mutual influence of social innovation factors and the influence weights of these factors are investigated. The results suggest that a quick response to market demand for sustainable products is the most influential factor in attaining social sustainability innovation during the pandemic. This article is concluded by providing insights for industrial experts and decision-makers to understand the underpinnings of social sustainability innovation during unforeseen situations.

Assessing suppliers considering social sustainability innovation factors during COVID-19 disaster.

Innovation can be considered one of the fundamental elements for ensuring sustainability. Companies have started to enhance their sustainability level through the application of innovative practices. The importance of employing innovative social sustainability practices within the supply chain seems to have escalated with the advent of COVID-19. However, studies focusing on the social aspect of sustainability innovation when selecting suppliers during the COVID-19 disaster are non-existent. Selecting these types of suppliers can significantly help companies to be more socially innovative and obtain sustainable development targets. This work introduces a social sustainability innovation framework for assessing suppliers during the COVID-19 pandemic. A group grey-best worst method (Group GBWM) is utilized to identify the criteria weights and improved grey relational analysis (IGRA) is utilized for ranking the suppliers. Findings show that "safety and health practices", "remote working conditions", and "localization" are the most important social sustainability innovation criteria, respectively, in choosing suppliers during COVID-19. A manufacturing firm is utilized as an example for verifying the efficiency of the proposed model and framework. This work helps industrial experts and researchers to better understand and focus on the social aspect of sustainable innovation, particularly when selecting suppliers during the critical COVID-19 pandemic situation.

Developing A Sustainable Urban-Environmental Quality Evaluation System in China Based on A Hybrid Model.

In China, with the acceleration of urbanization, people pay more attention to the quality of urban environment. Air pollution, vegetation destruction, water waste and pollution, and waste sorting have restricted the sustainable development of urban environment. It is important to evaluate the impact of these environmental concerns as a prerequisite to implement an effective urban environmental sustainability policy. The aim of this paper is to establish a system for evaluating sustainable urban environmental quality in China. We extracted six dimensions and 29 criteria for assessing urban sustainable environment. Then, a fuzzy technique and the best worst method were applied to obtain the weights for the dimensions and criteria. Next, grey possibility values were applied to evaluate the sustainable environmental quality of five cities: Beijing, Shanghai, Shenzhen, Guangzhou, and Hangzhou in China. A sensitivity analysis was performed to identify how the ranking of these five cities changed when varying the weights of each criterion. The results show that pollution control, the natural environment, and water management are the three most important dimensions for urban environmental quality evaluation. We suggest that controlling pollutant emissions, strengthening food waste management, improving clean production processes, and utilizing heat energy are the effective measures to improve the urban environment and achieve sustainable urban environmental development.