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Prout TA, Zilcha-Mano S, Aafjes-van Doorn K, et al. Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach. Front Psychol. 2020;11:586202doi: 10.3389/fpsyg.2020.586202.
Prout, T. A., Zilcha-Mano, S., Aafjes-van Doorn, K., Békés, V., Christman-Cohen, I., Whistler, K., Kui, T., & Di Giuseppe, M. (2020). Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach. Frontiers in psychology, 11586202. https://doi.org/10.3389/fpsyg.2020.586202
Prout, Tracy A, et al. "Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach." Frontiers in psychology vol. 11 (2020): 586202. doi: https://doi.org/10.3389/fpsyg.2020.586202
Prout TA, Zilcha-Mano S, Aafjes-van Doorn K, Békés V, Christman-Cohen I, Whistler K, Kui T, Di Giuseppe M. Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach. Front Psychol. 2020 Nov 05;11:586202. doi: 10.3389/fpsyg.2020.586202. eCollection 2020. PMID: 33240178; PMCID: PMC7682196.
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Front Psychol. 2020 Nov 05;11:586202. doi: 10.3389/fpsyg.2020.586202. eCollection 2020.

Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach.

Frontiers in psychology

Tracy A Prout, Sigal Zilcha-Mano, Katie Aafjes-van Doorn, Vera Békés, Isabelle Christman-Cohen, Kathryn Whistler, Thomas Kui, Mariagrazia Di Giuseppe

Affiliations

  1. School-Clinical Child Psychology Program, Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, United States.
  2. Department of Psychology, University of Haifa, Haifa, Israel.
  3. Clinical Psychology Program, Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, NY, United States.
  4. Department of Surgical, Medical and Molecular Pathology and of Critical Care Medicine, University of Pisa, Pisa, Italy.

PMID: 33240178 PMCID: PMC7682196 DOI: 10.3389/fpsyg.2020.586202

Abstract

Scientific understanding about the psychological impact of the COVID-19 global pandemic is in its nascent stage. Prior research suggests that demographic factors, such as gender and age, are associated with greater distress during a global health crisis. Less is known about how emotion regulation impacts levels of distress during a pandemic. The present study aimed to identify predictors of psychological distress during the COVID-19 pandemic. Participants (

Copyright © 2020 Prout, Zilcha-Mano, Aafjes-van Doorn, Békés, Christman-Cohen, Whistler, Kui and Di Giuseppe.

Keywords: COVID-19 pandemic; anxiety; defense mechanisms; depression; emotion regulation; machine learning; post-traumatic stress; somatization

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