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6"title","abstract","authors","link","date","subject","source","initial_decision","q0","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","exclusion_reason","extraction_date","expert_decision","ID","o1"
"COVID-19: Affect recognition through voice analysis during the winter lockdown in Scotland","The COVID-19 pandemic has led to unprecedented restrictions in people's lifestyle which have affected their psychological wellbeing. In this context, this paper investigates the use of social signal processing techniques for remote assessment of emotions. It presents a machine learning method for affect recognition applied to recordings taken during the COVID-19 winter lockdown in Scotland (UK). This method is exclusively based on acoustic features extracted from voice recordings collected through home and mobile devices (i.e. phones, tablets), thus providing insight into the feasibility of monitoring people's psychological wellbeing remotely, automatically and at scale. The proposed model is able to predict affect with a concordance correlation coefficient of 0.4230 (using Random Forest) and 0.3354 (using Decision Trees) for arousal and valence respectively. Clinical relevance: In 2018/2019, 12% and 14% of Scottish adults reported depression and anxiety symptoms. Remote emotion recognition through home devices would support the detection of these difficulties, which are often underdiagnosed and, if untreated, may lead to temporal or chronic disability.","Sofia de la Fuente Garcia; Fasih Haider; Saturnino Luz","https://medrxiv.org/cgi/content/short/2021.05.05.21256668","20210509","","medRxiv","Undecided","","","","","","","","","","","","","False","False","","","","","False","False","False","","False","False","False","False","False","False","False","False","False","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2021-05-10","",13590,""
"Exploring the frequency of anxiety and depression symptoms in a brazilian sample during the covid-19 outbreak","","","https://doi.org/10.3390/ijerph18094847","20210501","","Scopus","Undecided","","","","","","","","","","","","","False","False","","","","","False","False","False","","False","False","False","False","False","False","False","False","False","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2021-05-10","",13591,""
"The evolution in anxiety and depression with the progression of the pandemic in adult populations from eight countries and four continents","","","https://doi.org/10.3390/ijerph18094845","20210501","","Scopus","Undecided","","","","","","","","","","","","","False","False","","","","","False","False","False","","False","False","False","False","False","False","False","False","False","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2021-05-10","",13592,""
"Pandemic-induced fear and stock market returns: Evidence from China","We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.","","https://doi.org/10.1016/j.gfj.2021.100644","20210101","","Scopus","Undecided","","","","","","","","","","","","","False","False","","","","","False","False","False","","False","False","False","False","False","False","False","False","False","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2021-05-10","",13593,""