AUTHOR=Portugal Liana C. L. , Gama Camila Monteiro Fabricio , Gonçalves Raquel Menezes , Mendlowicz Mauro Vitor , Erthal Fátima Smith , Mocaiber Izabela , Tsirlis Konstantinos , Volchan Eliane , David Isabel Antunes , Pereira Mirtes Garcia , Oliveira Leticia de 

TITLE=Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

JOURNAL=Frontiers in Psychiatry

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.752870

DOI=10.3389/fpsyt.2021.752870

ISSN=1664-0640

ABSTRACT=<p><bold>Background:</bold> Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.</p><p><bold>Objective:</bold> The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.</p><p><bold>Methods:</bold> A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r<sup>2</sup>), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.</p><p><bold>Results:</bold> Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.</p><p><bold>Conclusions:</bold> Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.</p>