ABSTRACT
As thousands of 2019 Corona virus disease (Covid-19) clinical trials are continuously getting added to various registries these days, good practices on data sharing and transparency have become one of the prime topics of discussion than ever before. Although trial registration is considered a crucial step, there is a lack of integration between registration and published literature. Trial outcomes are a matter of public interest, but sponsor compliances are not adequate with the recommended guidelines. Although the global recognition of data transparency increases day by day, there is still a long journey to travel. It is high time that scholarly publishing stakeholders should put in a collaborative effort to check author compliance. In this article, we aimed to comprehend and discuss the imperative roles of various scholarly publishing stakeholders in improving clinical trial transparency during this pandemic situation and highlight the changing paradigm towards the pressing need for reporting clinical trial data more effectively.
ABSTRACT
Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students’ mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students’ learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils’ feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students’ feelings. Students’ favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Recently, with the emergence of world pandemic called COVID-19 virus all over the world, dental practitioners have stood out as high risked front liners. The aim of this study is to analyse the knowledge and management of emergency and safety precautions implemented by dentists during the pandemic of COVID-19 in Saudi Arabia. An online survey was used for this cross-sectional study using google forms and was distributed to dental professionals who works in government hospitals, private clinics, and academic universities in Saudi Arabia. Statistical evaluation was done using the data that was obtained from 355 dentists (academicians, private practitioners, military and government employees), with the power of the sample being 0.85. Relevant awareness regarding the incubation period and symptoms of COVID-19 virus was observed among the dental professionals. Preparedness and perception among dental professionals seem to be satisfactory and statistically significant. Obligatory improvements should be provided through educational campaigns.