ABSTRACT
Objective: The objectives of this study were to measure the global impact of the pandemic on the volumes for intravenous thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with two control 4-month periods. Background: The COVID-19 pandemic led to widespread repercussions on the delivery of health care worldwide. Design/Methods: We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by ICD-10 codes and/or classifications in stroke center databases. Results: There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95%CI,-11.7 to-11.3, p<0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95%CI,-13.8 to-12.7, p<0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95%CI,-13.7 to-10.3, p=0.001). There were greater declines in primary compared to comprehensive stroke centers (CSC) for stroke hospitalizations (-17.3% vs-10.3%, p<0.0001) and IVT (-15.5% vs-12.6%, p=0.0001). Recovery of stroke hospitalization volume (9.5%, 95%CI 9.2-9.8, p<0.0001) was noted over the two later (May, June) versus the two earlier (March, April) months of the pandemic, with greater recovery in hospitals with lower COVID-19 hospitalization volume, high volume stroke center, and CSC. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.3% (1,722/52,026) of all stroke admissions. Conclusions: The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months, with greater recovery in hospitals with lower COVID-19 hospitalizations, high volume stroke centers, and CSCs.
ABSTRACT
Problem Statement: A newly discovered infectious disease, known as COVID-19, has spread across the world with alarming speed. This resulted in shifting from face-to-face to online instruction and provoking stress and anxiety for many physical education teachers. Purpose: Grounded in transformative learning theory, we examined physical education teachers’ perceptions and experiences online teaching during the COVID-19 pandemic. Method: The participants in the present study consisted of four male and four female teachers who were all certified in teaching PE from several different regions of the United States. A semi-structured interview was employed. Five participants teach at the public elementary school and three participants teach at the public middle school. The following questions guided this study: (a) what are the perceptions of physical education teachers when they are requested online teaching?;(b) what are the experiences of physical education teachers when transitioning from face-to-face physical education to online teaching? and;(c) Did your perspectives change after online teaching? The data were analyzed by open, axial, and selective coding. Results: The following themes emerged from the analysis: (a) Not excited about online teaching: Lack of technological knowledge;(b) Benefits and challenges of online teaching;(c) Main goal: engage in physical activity;(d) Marginalization: physical education is not valued and;(e) Longing for face-to-face physical education. Conclusions: The findings of the current study showed that participants expressed unfavorable perceptions of online teaching during the COVID-19 pandemic. The results of this study revealed that all of the participants looked forward to teaching face-to-face physical education because they missed in-person relationships and interactions with their students. © 2021, Editura Universitatii din Pitesti. All rights reserved.
ABSTRACT
The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation;similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer’s face image taken in an unconstrained environment to facilitate the smart store’s extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.