Moral Distress, Perceived Stress, and Quality of Life During the COVID-19 Pandemic: A Hybrid SEM–ANN Approach

Cruz GARCIA Lirios

Authors

Cruz GARCIA Lirios Universidad de la Salud

Keywords:

Moral distress; perceived stress; quality of life; COVID-19 pandemic; structural equation modeling; artificial neural networks

Abstract

The COVID-19 pandemic generated unprecedented ethical challenges that affected not only health systems but also individual well-being. Beyond clinical outcomes, the crisis intensified moral distress and psychological stress, with potential consequences for quality of life. This study examines the relationships among moral distress, perceived stress, and quality of life in the context of the COVID-19 pandemic using a hybrid methodological approach that integrates Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN). Data were collected through standardized instruments assessing moral distress, perceived stress, and quality of life across physical, psychological, social, and environmental domains. SEM was employed to confirm the measurement models and to test direct and indirect effects, while ANN was used to explore non-linear patterns and predictive relationships among latent constructs. The findings provide evidence that higher levels of moral distress are associated with increased perceived stress, which in turn is linked to lower quality of life across all domains. The hybrid SEM–ANN approach offers a robust framework for understanding complex psychosocial processes during health emergencies and supports the development of ethically informed interventions aimed at protecting quality of life in crisis contexts.

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