COVID-19 Relates to Both PTSD and PTG in a Non-clinical Population, Why?

We assessed levels of post-traumatic stress disorder (PTSD) and post-traumatic growth (PTG) during the COVID-19 pandemic in the UK using an online questionnaire, in May and June 2020, during "lockdown.” Some 440 participants took part and 91.8% of the sample was female. Large numbers of participants had high levels of PTSD, with some 50.2% with "probable PTSD”. Yet 49.5% of participants also demonstrated high levels of PTG. Regression analysis revealed rumination to be key predictors for PTSD and PTG. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

An Exploration of Psychological Distress, Employment, and Housing Among Transgender and Gender Diverse Individuals During the COVID-19 Pandemic

Public Significance Statement The current study explored the ways that housing and employment instability brought on by the COVID-19 pandemic contributed to heightened psychological distress among a sample of transgender and gender diverse (TGD) individuals collected using an online survey platform. Our findings indicate that TGD individuals experienced heightened levels of psychological distress directly related to employment and housing instability which is a crucial and timely concern given that TGD individuals experienced greater psychological distress, unemployment, and housing instability relative to the general population prior to the pandemic. Receiving a COVID-19 diagnosis or experiencing symptoms associated with COVID-19 also predicted heightened levels of psychological distress. It is recommended that additional TGD-specific protections be implemented and expanded upon in order to meet the current and impending needs of TGD individuals. Transgender and gender diverse (TGD) individuals are faced with various societal factors that contribute to health disparities. As COVID-19 spreads, health disparities that existed prior to the pandemic have become exacerbated. The current study aims to address factors that may be negatively impacting TGD individuals during the COVID-19 pandemic by exploring their experiences related to symptoms of depression, symptoms of anxiety, employment, and housing. TGD participants (N = 342) were recruited from an online participant recruitment platform and answered questions related to their psychological well-being and changes in employment and housing since the start of the pandemic. Adverse changes in employment since the start of the pandemic were reported by almost 1/3 of participants and were associated with higher depression and anxiety. Changes in housing were reported by almost 1/4 of participants and were associated with higher anxiety. Experiencing COVID-19 symptoms or receiving a COVID-19 diagnosis were also associated with higher anxiety. Findings indicate TGD individuals who experienced changes in employment and housing since the start of the COVID-19 pandemic experience greater levels of psychological distress. Findings may inform mental health providers and policymakers on the repercussions the pandemic has had on TGD individuals.

Deep Recurrent Neural Networks with Attention Mechanisms for Respiratory Anomaly Classification

In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses.