Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning

Nooripour, R, Hosseinian, S, Hussain, A, Annabestani, M, Maadal, A, Radwin, LE, Hassani-Abharian, P, Pirkashani, NG and Khoshkonesh, A (2021) How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning. Journal of Religion and Health. ISSN 0022-4197

[img] Text
Final paper.doc.pdf - Accepted Version
Restricted to Repository staff only until 4 January 2022.

Download (1MB)

Abstract

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.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Journal of Religion and Health. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10943-020-01151-z
Uncontrolled Keywords: 1117 Public Health and Health Services
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Computer Science & Mathematics
Publisher: Springer Science and Business Media LLC
Date Deposited: 18 Jan 2021 09:54
Last Modified: 18 Jan 2021 10:00
DOI or Identification number: 10.1007/s10943-020-01151-z
URI: https://researchonline.ljmu.ac.uk/id/eprint/14273

Actions (login required)

View Item View Item