ABSTRACT
The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that 'fear' and 'sadness' occurred during those times. © 2022 IEEE.
ABSTRACT
Background: Despite a marked decline in worldwide under-five mortality over the past 30 years, the largest proportion of these deaths remain neonates. In 2018, 4 million (75% of all under-five deaths) infants died worldwide [1]. The neonatal mortality rate in Ethiopia was 28.1 per 100 live births, compared to 17.7 globally and 27.2 in Africa, ranking 23rd for highest neonatal mortality [2]. The Ethiopian Pediatric Society (EPS), entered a virtual partnership with the American Academy of Pediatrics (AAP) in 2020 to provide educational and practical support to clinicians in Ethiopia to perform quality improvement (QI) research. QI training for clinicians at eight Ethiopian sites is modeled on Project ECHO (Extension for Community Healthcare Outcomes) [3]. ECHO improves capacity for specialists to deliver care to underserved communities via collaboration with local clinicians using video teleconferencing, which has been a particularly advantageous tool to continue global health work during the COVID-19 pandemic. The goal of this project is to support local Ethiopian providers in designing, implementing, and assessing a QI intervention. Methods: We adapt the Project ECHO model to have U.S. neonatology faculty-fellow teams mentor clinicians from 8 hospitals in Ethiopia. Using video conferencing and regular contact through mobile devices, teams work to identify gaps in care, create SMART aim statements, identify key stakeholders and barriers to change, and implement interventions. Interventions are measured with a phone application, Liveborn, which allows instant transmission of data internationally. Sites provide monthly progress updates to the EPS. AAP/EPS leadership, U.S.-based faculty-fellow teams, and QI team leaders meet monthly via video conference for didactic sessions on QI methodology followed by teams presenting their progress, which promotes discussion and collaboration between the sites, AAP/EPS leadership, and U.S. mentorship teams to address any barriers. Results: Teams identified compliance with delayed cord clamping (DCC) and skin-to-skin (STS) after delivery as a gap by independently collecting local data. QI teams are currently still implementing interventions and measuring improvement. Teams have identified several barriers, the most common cited being stakeholder buy-in and logistic challenges in implementation. During monthly meetings, teams continually discuss and brainstorm ways to address barriers overall and among individual sites. Conclusion: Via the ECHO model, the AAP and EPS are supporting Ethiopian QI teams in designing and implementing QI interventions despite the limitations imposed by the COVID-19 pandemic. Challenges to this mentorship process include communication barriers (language, internet network) and perception of lack of need by Ethiopian clinicians for U.S. mentorship. Using the ECHO model, all teams have defined SMART aims, begun to implement interventions, and are now collecting compliance data.