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

Background: 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. // Objective: 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. // Methods: 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 (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels. // Results: 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. // Conclusions: 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

The hidden snake in the grass: superior detection of snakes in challenging attentional conditions

Snakes have provided a serious threat to primates throughout evolution. Furthermore, bites by venomous snakes still cause significant morbidity and mortality in tropical regions of the world. According to the Snake Detection Theory(SDT Isbell, 2006; 2009), the vital need to detect camouflaged snakes provided strong evolutionary pressure to develop astute perceptual capacity in animals that were potential targets for snake attacks. We performed a series of behavioral tests that assessed snake detection under conditions that may have been critical for survival. We used spiders as the control stimulus because they are also a common object of phobias and rated negatively by the general population, thus commonly lumped together with snakes as ''evolutionary fear-relevant''. Across four experiments (N5205) we demonstrate an advantage in snake detection, which was particularly obvious under visual conditions known to impede detection of a wide array of common stimuli, for example brief stimulus exposures, stimuli presentation in the visual periphery, and stimuli camouflaged in a cluttered environment. Our results demonstrate a striking independence of snake detection from ecological factors that impede the detection of other stimuli, which suggests that, consistent with the SDT, they reflect a specific biological adaptation. Nonetheless, the empirical tests we report are limited to only one aspect of this rich theory, which integrates findings across a wide array of scientific disciplines