Search

The dramatic changes in people's daily lives caused by the 2019 coronavirus (COVID-19) pandemic have had a huge impact on their emotions and behaviors. This study aimed to examine psychosocial responses to COVID-19 using network analysis. A total of 1,500 urban residents of South Korea, selected from an online public panel, were surveyed using self-rating questionnaires addressing daily life changes, fear of infection, and distress related to COVID-19. Participants also completed a 10-item Perceived Stress Scale survey. We constructed regularized partial correlation networks, estimated global and local network metrics, tested network accuracy and stability, and compared the estimated networks between men and women. The network of the psychosocial responses consisted of 24 nodes that were classified into five groups: 'fear of infection', 'difficulty with outside activities', 'economic loss', 'altered eating and sleeping', and 'adaptive stress'. The node centralities indicated that 'distress in obtaining daily necessities' and 'concern about harming others' were the most important issues in people's responses to COVID-19. These nodes were connected by a negative edge, reflecting individual- and community-level issues, respectively. The overall level of perceived stress was linked to the network by the connection node 'anger toward others or society', which was associated with economic problems in men, but with distress from changes in daily activities in women. The results suggest that two contrasting feelings—personal insecurity regarding basic needs and a collectivistic orientation—play roles in the response to unusual experiences and distress due to COVID-19. This study also showed that public anger could arise from the psychological stress under the conditions imposed by COVID-19. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to examine the effectiveness of aromatherapy with ginger essential oil on nausea and vomiting in abdominal surgery patients. Method: This was a quasi-experimental study with a nonequivalent control group and repeated measures. The experimental group ( n = 30) received ginger essential oil inhalation. The placebo control group ( n = 30) received normal saline inhalation. The level of postoperative nausea and vomiting was measured using a Korean version of the Index of Nausea, Vomiting, and Retching (INVR) at baseline and at 6, 12, and 24 h after aromatherapy administration. The data were collected from July 23 to August 22, 2012. Results: Nausea and vomiting scores were significantly lower in the experimental group with ginger essential oil inhalation than those in the placebo control group with normal saline. In the experimental group, the nausea and vomiting scores decreased considerably in the first 6 h after inhaled aromatherapy with ginger essential oil. Conclusion: Findings indicate that ginger essential oil inhalation has implications for alleviating postoperative nausea and vomiting in abdominal surgery patients. [ABSTRACT FROM AUTHOR]

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors in geostationary orbits, which provide more frequent observations at higher spatial resolutions. Several investigations have reported that extrapolation-based prediction can be more effective than numerical weather model-based prediction for a short lead time of less than a few hours. This study introduces the algorithm based on extrapolation method to produce QPN products for Advanced Meteorological Imager (AMI) onboard South Korea's second geostationary satellite (GEO-KOMPSAT-2A). The algorithm first provides the 3-hour potential accumulated rainfall (PAR) for a very-short-range forecast using extrapolation and then calculates the probability of rainfall (POR) during the same time period through statistical methods. The algorithm for PAR first identifies rainfall features and computes the motion vectors of the identified rainfall features through tracking between two consecutive GEO-KOMPSAT-2A rain rate images. The algorithm can then extrapolate future rainfall rates from current and previous rainfall rates outputs. In addition, the extrapolated rainfall fields are used as inputs for the POR estimation process, which produces the rainfall probability during the same 3-h period. POR is estimated based on rainfall frequency and adjusted by neighboring rainfall feature information. POR can provide a quantitative measure for the uncertainty in the PAR estimates. Retrieved PAR and POR from the QPN algorithm are validated with 3-h accumulated rainfalls obtained from the GEO-KOMPSAT-2A rain rate products. Forecasting accuracy of the PAR algorithm with time has also been investigated. The accuracy tends to decrease with increasing lead time, as expected. It may be due to the limitations of the extrapolation technique and degradation of the accuracy of the PAR estimates. The details on the improvements from the prototype algorithm and validation statistics will be discussed.AcknowledgementThis work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B127555-02). [ABSTRACT FROM AUTHOR]