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首页> 外文期刊>Frontiers in Psychology >Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment
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Stress, Coping, and Resilience Before and After COVID-19: A Predictive Model Based on Artificial Intelligence in the University Environment

机译：Covid-19之前和之后的压力，应对和弹性：基于大学环境中人工智能的预测模型

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The COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people (73% women) from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences (version 24) was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic (before and after the implementation of isolation measures), with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community.
机译：Covid-19全球健康紧急情况极大地影响了教育领域。面对前所未有的压力情况，教授，学生和家庭在整个监禁期间都雇用了各种应对和恢复力战略。高和持续的压力水平与其他病理相关;因此，需要他们的检测和预防。因此，本研究旨在基于人工智能的教育领域在教育领域进行预测模型，包括某些社会渗透变量，应对策略和抵御能力，以及研究它们之间的关系。使用非概率雪球采样方法，涉及西班牙东南部大学教育界的337人（73％）。感知压力规模，压力管理问卷和简要的弹性量表进行了管理。社会科学（版本24）的统计包装用于设计人工神经网络的体系结构。结果发现，通过突触策略和流行病前后的时间（实施前后的措施之前和实施）的突触权来预测应力水平，在神经网络模型中发现了超过80％的预测能力。此外，在使用某些应对策略，压力水平和弹性之间鉴定了直接和重要的协会。该研究的结论对于有效的压力检测至关重要，因此，通过讨论弹性或缺乏对压力水平预测的影响，提前干预教育心理学领域。识别在压力水平中保持更大预测力的变量是设计更多调整预防计划并预测社区需求的有效策略。

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