Qualitative Investigation of Educator and Parent Experiences of Education Services During COVID-19

The COVID-19 pandemic has resulted in a worldwide disruption of education service systems. A significant gap has emerged in understanding the impact of these changes to both educational services and child development. The current qualitative study presents interviews with early childhood educators and parents to capture their experiences with educational service delivery for young children with disabilities during the pandemic. The study included nine early childhood educators and nine parents with children between the ages of 3 and 6 who received preschool special education services during the pandemic. Themes that emerged from the interviews include changes to the modality and intensity of services, barriers and challenges related to service delivery, and benefits resulting from shifts to educational services. All findings will be discussed with an eye toward informing future practice related to family-centered services.

The Routledge handbook of media education futures post-pandemic

This handbook showcases how educators and practitioners around the world adapted their routine media pedagogies to meet the challenges of the COVID-19 pandemic, which often led to significant social, economic, and cultural hardships. Combining an innovative mix of traditional chapters, autoethnography, case studies, and dialogue within an intercultural framework, the handbook focuses on the future of media education and provides a deeper understanding of the challenges and affordances of media education as we move forward. Topics range from fighting disinformation, how vulnerable communities coped with disadvantages using media, transforming educational TV or YouTube to reach larger audiences, supporting students' wellbeing through various online strategies, examining early childhood, parents, and media mentoring using digital tools, reflecting on educators' intersectionality on video platforms, youth-produced media to fight injustice, teaching remotely and providing low-tech solutions to address the digital divide, search for solutions collaboratively using social media, and many more. Offering a unique and broad multicultural perspective on how we can learn from the challenges of addressing varied pedagogical issues that have arisen in the context of the pandemic, this handbook will allow researchers, educators, practitioners, institution leaders, and graduate students to explore how media education evolved during 2020 and 2021, and how these experiences can shape the future direction of media education. © Shilpy Lather, 2022. All rights reserved.

Dual Mediating Effects of Information and Communication Technology Resources and Family Satisfaction on COVID-19 Knowledge Among Middle School Students

Little is known about underlying pathways regarding how middle school students can improve their COVID-19 knowledge. This study investigates the relationship between subjective poverty and COVID-19 knowledge by considering a dual mediation model. A total of 328 middle school students were included. We used a multiple mediator model developed by Preacher and Hayes, using bootstrapping approach to include a first and sequence mediators. We employed the PROCESS macro 3.4 for SPSS to test the dual mediating effects. Both Information and Communication Technology (ICT) resources and family satisfaction mediated the association between subjective poverty and knowledge about COVID-19. Further, an indirect effect of subjective poverty via the dual mediators of ICT resources and family satisfaction was found. Further, middle school students who more satisfied with their family dynamics and who had more ICT resources were more likely to have greater levels of knowledge about COVID-19. This study contributes to further understanding of pathways between subjective poverty and COVID-19 knowledge in the context of dual mediators. It is imperative to help households in poverty before and since COVID-19;specific financial supports that focus on ICT resources should be provided to increase middle school students' quality of online learning, which then helps to improve family satisfaction. The pathways influence COVID-19 knowledge.

Connecting through flipgrid: Examining social presence of english language learners in an online course during the pandemic

This study focuses on the social presence framework (Rourke et al., 2001), in order to examine the ways that university-level international students develop social interaction and support in a virtual asynchronous learning community in an online class during the COVID-19 pandemic. English language learners (ELLs) participated in weekly online exchanges on a video discussion platform called Flipgrid in the form of oral dialogue journals for reflection on their academic learning and experiences during these disruptive times. These ELLs’ video journals and peer responses (N = 198) were collected for content analysis, in order to investigate how the use of video-based asynchronous computer-mediated communication (ACMC) can establish positive social and emotional support and a sense of community. The findings of the study indicate that ACMC was successful in establishing interconnectedness in terms of high levels of self-disclosure, positive facial expressions, and other indicators of social and emotional support, demonstrating social presence. Implications of the findings are discussed in terms of how social presence is expressed and fostered in video-based ACMC communities during emergency remote teaching. © 2022, equinox publishing.

PATTERNS OF LIVER TRANSPLANTATION EVALUATIONS FOR HOSPITALIZED ADULTS WITH ACUTE ALCOHOLIC HEPATITIS DURING COVID-19: A SINGLE CENTER STUDY

Background: Acute alcoholic hepatitis (AH) is a clinical syndrome observed in patients with alcohol-associated liver disease (ALD) defined by jaundice, recent heavy drinking, characteristic liver enzyme patterns. It is characterized by poor short-term survival without liver transplantation (LT). Multiple studies have suggested an association between lockdown, higher rates of drinking among patients with alcohol use disorder, and higher rates of relapse among those who are abstinent. In light of these observations, we aimed to characterize patterns of inpatient LT evaluations for AH during COVID-19 at a large-volume LT academic center. Methods: We conducted a retrospective chart review of all inpatient LT evaluations from September 2019, when the AH LT protocol was approved at our hospital, through August 2020. AH was defined as onset of jaundice within 8 weeks of last alcohol use in patients with ongoing excessive alcohol consumption;AST >50 IU/L, AST:ALT ratio of >1.5 and both values <400 IU/L, and total bilirubin of >3.0 mg/dL, or a liver biopsy with steatohepatitis. Patients were followed until 9/30/2020. The monthly number and proportion of evaluations performed for AAH were determined and compared before and after stay-at-home measures were enforced in California (3/19/2020). The subset of patients evaluated for AH was further characterized. Results: Between 09/2019 and 08/2020, 290 hospitalized patients were evaluated for LT. From 09/2019 to 01/2020, 24 to 34 inpatient LT evaluations were performed per month. The numbers declined surrounding the onset of COVID-19 pandemic: from February through April, 18, 15, and 9 inpatient evaluations were performed respectively each month. However, by May 2020, numbers returned to their pre-COVID-19 baseline (Figure 1). In the prespecified study period, 56 (19.3%) patients underwent LT evaluation for AH. Their mean age was 43.4 years. There were 29 men (51.7%) and the majority were White (n=29, 51.7%), followed by Latinx (n=19, 32.1%). 29 patients (51.7%) were on hemodialysis. The mean total bilirubin was 24.0 mg/dL. The proportion of LT evaluations performed for AH increased from 49.4% before the stay-at-home order to 82.4% (p-value=0.01) in April 2020 immediately following it. This proportion promptly returned to baseline in May (Figure 2). Still, a minority of all patients with AH ultimately underwent LT or listing (n=8, 16%). Conclusion: LT evaluation practices for hospitalized patients changed in the early stages of the COVID-19 pandemic but returned to the pre-COVID-19 baseline by May of 2020 following improved understanding of COVID-19 and implementation of hospital practices. Hepatologists should remain vigilant and counsel their patients of alcohol misuse during the pandemic, especially given increases during the Fall of 2020.

Rapid cycle system improvement for COVID-19 readiness: integrating deliberate practice, psychological safety and vicarious learning

Introduction In the face of a rapidly advancing pandemic with uncertain pathophysiology, pop-up healthcare units, ad hoc teams and unpredictable personal protective equipment supply, it is difficult for healthcare institutions and front-line teams to invent and test robust and safe clinical care pathways for patients and clinicians. Conventional simulation-based education was not designed for the time-pressured and emergent needs of readiness in a pandemic. We used 'rapid cycle system improvement' to create a psychologically safe learning oasis in the midst of a pandemic. This oasis provided a context to build staff technical and teamwork capacity and improve clinical workflows simultaneously. Methods At the Department of Anaesthesia and Intensive Care in Prince of Wales Hospital, a tertiary institution, in situ simulations were carried out in the operating theatres and intensive care unit (ICU). The translational simulation design leveraged principles of psychological safety, rapid cycle deliberate practice, direct and vicarious learning to ready over 200 staff with 51 sessions and achieve iterative system improvement all within 7 days. Staff evaluations and system improvements were documented postsimulation. Results/Findings Staff in both operating theatres and ICU were significantly more comfortable and confident in managing patients with COVID-19 postsimulation. Teamwork, communication and collective ability to manage infectious cases were enhanced. Key system issues were also identified and improved. Discussion To develop readiness in the rapidly progressing COVID-19 pandemic, we demonstrated that 'rapid cycle system improvement' can efficiently help achieve three intertwined goals: (1) ready staff for new clinical processes, (2) build team competence and confidence and (3) improve workflows and procedures.

US Based COVID-19 Tweets Sentiment Analysis Using TextBlob and Supervised Machine Learning Algorithms

In December 2019, a highly contagious disease, Coronavirus disease 2019 (COVID-19) was first detected in Wuhan, China. The disease has spread to 212 countries and territories worldwide. While this epidemic has continued to infect millions of people, several nations have resorted to complete lockdowns. People took social networks during this shutdown to share their opinions, feelings, and find a way to calm down. This study proposed a US-based sentiment analysis of the tweets using machine learning and the lexicon analysis approach. This US-based tweets dataset was collected by RStudio software from 30 January 2020 to 10th May 2020, contains 11858 tweets. We find the label corresponding to each tweet using TextBlob, that is to say, positive, negative, or neutral. To clean up the facts we pre-process the tweets. In a later step, different feature techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF) are used to preserve expressive information. Finally, the random forest, gradient boosting machine, extra tree classifier, logistic regression, and support vector machine models are used to categorize beliefs as being positive, negative, or neutral. Our suggested pipeline output is assessed using accuracy, precision, recall F1 score. This research study shows how TF-IDF features can increase the performance of the supervised machine learning models and in this work, the gradient boosting machine outperforms the others and achieves high accuracy of 96% when paired with TF-IDF features. This analysis was done to analyze how the situation is being handled by citizens of the United States. The results of the experiments validate the approach's effectiveness. © 2021 IEEE.

An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification

Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts' image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. © 2021 Aijaz Ahmad Reshi et al.

Privacy in the Time of COVID-19: Divergent Paths for Contact Tracing and Route-Disclosure Mechanisms in South Korea

In response to COVID-19, Korea has implemented digital contact tracing and patient route disclosure schemes. While the former has been embraced more willingly, the latter has been shunned due to privacy concerns, demonstrating that privacy is highly dynamic and is of contextual value.