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Work Engagement, Work Environment, and Psychological Distress during the COVID-19 Pandemic: A Cross-Sectional Study in Ecuador.

Carlos Ruiz-FrutosIngrid Adanaqué-BravoMónica Ortega-MorenoJavier Fagundo-RiveraKenny Escobar-SegoviaCristian Arturo Arias-UlloaJuan Gómez Salgado
Published in: Healthcare (Basel, Switzerland) (2022)
Work environments can interfere with the mental health of workers as generators or reducers of psychological distress. Work engagement is a concept related to quality of life and efficiency at work. The aim of this study was to find the relationship between work environment factors and work engagement among the Ecuadorian general population during the first phase of the COVID-19 pandemic to assess their levels of psychological distress. For this purpose, a cross-sectional, descriptive study using a set of questionnaires was performed. Sociodemographic and work environment data, work engagement (UWES-9 scale) scores, and General Health Questionnaire (GHQ-12) scores were collected. The variables that predicted 70.2% of psychological distress during the first phase of the pandemic were being female, with a low level of vigour (work engagement dimension), being stressed at work, and low job satisfaction. The sample showed an intermediate level of engagement in both the global assessment and the three dimensions, being higher in those without psychological distress. With effective actions on work environment factors, mental health effects may be efficiently prevented, and work engagement may be benefited. Companies can reduce workers' psychological distress by providing safe and effective means to prevent the risk of contagion; reducing the levels of work conflict, work stress, or workload; and supporting their employees with psychological measures in order to maintain ideal working conditions.
Keyphrases
  • social media
  • mental health
  • sleep quality
  • healthcare
  • health information
  • public health
  • coronavirus disease
  • cross sectional
  • risk factors
  • climate change
  • machine learning
  • human health
  • heat stress