ABSTRACT
Numerous successes have been achieved in combating the COVID-19 pandemic, initially using various precautionary measures like lockdowns, social distancing, and the use of face masks. More recently, various vaccinations have been developed to aid in the prevention or reduction of the severity of the COVID-19 infection. Despite the effectiveness of the precautionary measures and the vaccines, there are several controversies that are massively shared on social media platforms like Twitter. In this paper, we explore the use of state-of-the-art transformer-based language models to study people's acceptance of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual tweets using relevant hashtags and keywords. Our analysis and visualizations revealed that most tweets expressed neutral sentiments about COVID-19 vaccines, with some individuals expressing positive views, and there was no strong preference for specific vaccine types, although Moderna received slightly more positive sentiment. We also found out that fine-tuning a pre-trained LLM with an appropriate dataset can yield competitive results, even if the LLM was not initially pre-trained on the specific language of that dataset.
Subject(s)
COVID-19ABSTRACT
BACKGROUND: Knowledge about the outcome of COVID-19 on pregnant women is so important. The published literature on the outcomes of pregnant women with COVID-19 is confusing. The aim of this study was to report our clinical experience about the effect of COVID-19 on pregnant women and to determine whether it was associated with increased mortality or an increase in the need for mechanical ventilation in this special category of patients. METHODS: This was a cohort study from some isolation hospitals of the Ministry of Health and Population, in eleven governorates, Egypt. The clinical data from the first 64 pregnant women with COVID-19 whose care was managed at some of the Egyptian hospitals from 14 March to 14 June 2020 as well as 114 non-pregnant women with COVID-19 was reviewed. RESULTS: The two groups did not show any significant difference regarding the main outcomes of the disease. Two cases in each group needed mechanical ventilation (p 0.617). Three cases (4.7%) died among the pregnant women and two (1.8%) died among the non-pregnant women (p 0.352). CONCLUSIONS: The main clinical outcomes of COVID-19 were not different between pregnant and non-pregnant women with COVID-19. Based on our findings, pregnancy did not exacerbate the course or mortality of COVID-19 pneumonia.
ABSTRACT
This work investigates the use of distance learning in saving students' academic year amid COVID-19 lockdown. It assesses the adoption of distance learning using various online application tools that have gained widespread attention during the coronavirus infectious disease 2019 (COVID-19) pandemic. Distance learning thrives as a legitimate alternative to classroom instructions, as major cities around the globe are locked down amid the COVID-19 pandemic. To save the academic year, educational institutions have reacted to the situation impulsively and adopted distance learning platforms using online resources. This study surveyed random undergraduate students to identify the impact of trust in formal and informal information sources, awareness and the readiness to adopt distance learning. In this study, we have hypothesized that adopting distance learning is an outcome of situational awareness and readiness, which is achieved by the trust in the information sources related to distance learning. The findings indicate that trust in information sources such as institute and media information or interpersonal communication related to distance learning programs is correlated with awareness (ß = 0.423, t = 12.296, p = 0.000) and contribute to readiness (ß = 0.593, t = 28.762, p = 0.001). The structural model path coefficient indicates that readiness strongly influences the adoption of distance learning (ß = 0.660, t = 12.798, p = 0.000) amid the COVID-19 pandemic. Our proposed model recorded a predictive relevance (Q2) of 0.377 for awareness, 0.559 for readiness, and 0.309 for the adoption of distance learning, which explains how well the model and its parameter estimates reconstruct the values. This study concludes with implications for further research in this area.