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1.
Obstetrics and Gynecology ; 139(SUPPL 1):87S, 2022.
Article in English | EMBASE | ID: covidwho-1925191

ABSTRACT

INTRODUCTION: Pregnant women are at increased risk of severe disease with COVID-19. Despite strong recommendations from ACOG and the Society for Maternal-Fetal Medicine for vaccination, COVID-19 vaccination hesitancy persists. With this study, we aim to evaluate opinions about the COVID-19 vaccine in a cohort of high-risk pregnant patients. METHODS: Institutional review board approval was obtained. Patients attending a regional Maternal-Fetal Medicine clinic were surveyed about the COVID-19 vaccine using a standardized questionnaire. Demographic, obstetrical, and medical information were ed using medical records. The vaccinated and unvaccinated groups were evaluated using student t-tests and a hierarchical Bayesian logistic regression. RESULTS: Among the 157 participants, 38.2% received the vaccine. There were no significant differences between the groups in age, BMI, employment, race and ethnicity, gestational age, or gravity. There was no correlation with influenza or Tdap vaccination rates, nor tobacco or alcohol use. Education level was negatively correlated with vaccination status and had the largest effect size. Those with education at or less than eighth grade level were least likely to be vaccinated (95% CI, 24.46 to 20.41). Those with children in the home were less likely to be vaccinated (95% CI, 21.36 to 20.59). Unvaccinated patients chose lack of data in pregnancy (66%) as their primary concern. Most patients prefer to learn about vaccines via conversation with their doctor (46.7% for vaccinated and 59.8% for unvaccinated). CONCLUSION: The vaccination rate is low in our population. A provider-initiated conversation about COVID-19 vaccination included with routine prenatal care could increase the vaccination rate.

2.
20th ACM Symposium on Document Engineering, DocEng 2020 ; 2020.
Article in English | Scopus | ID: covidwho-891514

ABSTRACT

The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature, there arose a need for organizing those documents. We describe an approach to organize and visualize the scientific literature on or related to COVID-19 using machine learning techniques so that papers on similar topics are grouped together. By doing so, the navigation of topics and related papers is simplified. We implemented this approach using the widely recognized CORD-19 dataset to present a publicly available proof of concept. © 2020 ACM.

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