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How resiliency and hope can predict stress of covid-19 by mediating role of spiritual well-being based on machine learning

Nooripour, R ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1007/s10943-020-01151-z
  3. Publisher: Springer , 2021
  4. Abstract:
  5. Nowadays, artificial intelligence (AI) and machine learning (ML) are playing a tremendous role in all aspects of human life and they have the remarkable potential to solve many problems that classic sciences are unable to solve appropriately. Neuroscience and especially psychiatry is one of the most important fields that can use the potential of AI and ML. This study aims to develop an ML-based model to detect the relationship between resiliency and hope with the stress of COVID-19 by mediating the role of spiritual well-being. An online survey is conducted to assess the psychological responses of Iranian people during the Covid-19 outbreak in the period between March 15 and May 20, 2020, in Iran. The Iranian public was encouraged to take part in an online survey promoted by Internet ads, e-mails, forums, social networks, and short message service (SMS) programs. As a whole, 755 people participated in this study. Sociodemographic characteristics of the participants, The Resilience Scale, The Adult Hope Scale, Paloutzian & Ellison’s Spiritual Wellbeing Scale, and Stress of Covid-19 Scale were used to gather data. The findings showed that spiritual well-being itself cannot predict stress of Covid-19 alone, and in fact, someone who has high spiritual well-being does not necessarily have a small amount of stress, and this variable, along with hope and resiliency, can be a good predictor of stress. Our extensive research indicated that traditional analytical and statistical methods are unable to correctly predict related Covid-19 outbreak factors, especially stress when benchmarked with our proposed ML-based model which can accurately capture the nonlinear relationships between the collected data variables. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature
  6. Keywords:
  7. Adult ; Artificial intelligence ; Human ; Iran ; Machine learning ; COVID-19 ; Humans ; SARS-CoV-2
  8. Source: Journal of Religion and Health ; Volume 60, Issue 4 , 2021 , Pages 2306-2321 ; 00224197 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10943-020-01151-z