ABSTRACT
The outbreak of the contagion corona virus disease has engrossed worldwide attention. The nature of the disease and its spread has put excessive burden on service providers leading to burn out. In the face of health threats and work pressure during pandemic, the current study aimed to investigate the impact of COVID-19 stress on Burnout among health care providers. Following a convenient sampling technique, a sample of 153 healthcare providers with an age ranged from 24 to 60 years were assessed with COVID Stress Scale (Taylor, et al., 2020) and Maslach Burnout Inventory (MBI;Maslach et al., 1997). SPSS 21 was used for statistical analysis of data. Findings revealed that Sub-Scales of COVID stress collectively explained 48% of variance in predicting emotional exhaustion and 39% variance in producing depersonalization among healthcare providers. However, COVID stress negatively predicted personal accomplishment among healthcare providers. Moreover, t-test revealed that female healthcare providers showed higher level of COVID stress i.e. danger, socio-economic consequence, xenophobia and compulsive checking as compared to males while non-significant gender differences were observed for contamination and traumatic stress. The study also found a higher level of personal accomplishment among male healthcare providers whereas female healthcare providers demonstrated higher level of emotional exhaustion and depersonalization in comparison to male health care providers © 2022, Pakistan Journal of Psychological Research.All Rights Reserved.
ABSTRACT
COVID-19 pandemic is the main reason people must wear face masks in public places. Traditionally, officers monitor the use of face masks in the public area manually. However, monitoring masks using manual techniques is challenging in a crowded spot. Thus, we propose a face mask detection based on Generative Adversarial Networks (GAN) through the learning model to accelerate mask detection accurately and quickly. To construct our detection model, we collect the dataset, conduct pre-processing, and train the model by tuning multiple parameters to obtain the highest accuracy and tiny loss. The experimental results can produce D_Loss = 0.0032 and G_Loss = 7.3296. Therefore, the proposed model can be a promising solution for mask detection issues.