Policy response for disadvantaged groups during the COVID-19 pandemic: Vietnam experiences

This article aims to describe the Vietnamese Government's policy response to support disadvantaged groups affected by the COVID-19 pandemic through the relief package. The Vietnamese policy response is characterized by rapid response, priority for disadvantaged groups in communities, social work core values in the policy, openness, and transparency. We hope the experiences from Vietnam could be helpful for similar settings in the world. (PsycInfo Database Record (c) 2022 APA, all rights reserved) (French) Cet article vise a decrire la reponse politique du gouvernement vietnamien pour soutenir les groupes defavorises affectes par la pandemie de COVID-19 a travers le programme de secours. La reponse politique vietnamienne se caracterise par une reponse rapide, la priorite donnee aux groupes defavorises dans les communautes, les valeurs fondamentales du travail social dans la politique, l'ouverture et la transparence. Nous esperons que les experiences du Vietnam pourraient etre utiles pour des contextes similaires dans le monde. (PsycInfo Database Record (c) 2022 APA, all rights reserved) (Spanish) Este articulo tiene como objetivo describir la respuesta politica del gobierno vietnamita para apoyar a los grupos desfavorecidos afectados por la pandemia de COVID-19 a traves del paquete de ayuda. La respuesta de la politica vietnamita se caracteriza por una respuesta rapida, prioridad para los grupos desfavorecidos en las comunidades, valores fundamentales del Trabajo Social en la politica, apertura y transparencia. Esperamos que las experiencias de Vietnam puedan ser utiles para entornos similares en el mundo. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Spatiotemporal Evolution of SARS-CoV-2 Alpha and Delta Variants during Large Nationwide Outbreak of COVID-19, Vietnam, 2021.

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions.

Usefulness of Hospital Admission Chest X-ray Score for Predicting Mortality and ICU Admission in COVID-19 Patients.

We aimed to investigate the performance of a chest X-ray (CXR) scoring scale of lung injury in prediction of death and ICU admission among patients with COVID-19 during the 2021 peak pandemic in HCM City, Vietnam. CXR and clinical data were collected from Vinmec Central Park-hospitalized patients from July to September 2021. Three radiologists independently assessed the day-one CXR score consisting of both severity and extent of lung lesions (maximum score = 24). Among 219 included patients, 28 died and 34 were admitted to the ICU. There was a high consensus for CXR scoring among radiologists (κ = 0.90; CI95%: 0.89-0.92). CXR score was the strongest predictor of mortality (tdAUC 0.85 CI95% 0.69-1) within the first 3 weeks after admission. A multivariate model confirmed a significant effect of an increased CXR score on mortality risk (HR = 1.33, CI95%: 1.10 to 1.62). At a threshold of 16 points, the CXR score allowed for predicting in-hospital mortality and ICU admission with good sensitivity (0.82 (CI95%: 0.78 to 0.87) and 0.86 (CI95%: 0.81 to 0.90)) and specificity (0.89 (CI95%: 0.88 to 0.90) and 0.87 (CI95%: 0.86 to 0.89)), respectively, and can be used to identify high-risk patients in needy countries such as Vietnam.

Use of Project ECHO Telementoring Model in Continuing Medical Education for Pediatricians in Vietnam: Preliminary Results.

The ECHO (Extension for Community Health Outcomes) model has been introduced and implemented in several hospitals and health programs in Vietnam since 2015. In 2018, Vietnam National Children's Hospital (VNCH) officially implemented the ECHO model to provide continuing medical education (CME) credits on pediatrics topics for medical staff in its satellite hospitals and health centers in the Northern region of Vietnam. This paper presents preliminary results of the ECHO program at VNCH. Methods included pre- and post-program assessments of pediatricians' clinical knowledge, self-efficacy, and professional satisfaction. The analysis compared the differences between pre/post scores descriptively. Knowledge of participants increased by 22.5% points on average. More than 90% of Project ECHO-Pediatrics participants experienced increased confidence. Overall, there was an improvement in participants' self-efficacy in the post-training compared to the pre-training (range 14.7%-22.6% difference from pre-training). All participants improved on their results in the clinical test immediately after the training and maintained it after 3 months. The study demonstrated the ability Project ECHO to improve healthcare worker knowledge and satisfaction.

The Covid-19 pandemic and food consumption at home and away: An exploratory study of English households.

The Covid-19 pandemic has changed attitudes of English households towards food consumption at home and when eating out. Little academic research has however examined the scope and the scale of these changes, especially in the context of foodservice provision. This mixed methods study explores the effect of Covid-19 on food consumption in English households at home and away. It reveals increased frequency and variety of cooking during lockdown as a driver of household food wastage. The study demonstrates public hesitance towards eating out post-Covid-19. Foodservice providers are expected to re-design their business settings and adopt protective and preventative measures, such as frequent cleaning and routine health checks, to encourage visitation. After the pandemic, increased preference towards consuming (more) sustainable food at home, but not when eating out, is established. These insights can aid grocery and foodservice providers in offering more tailored products and services in a post-pandemic future.

Policy response for disadvantaged groups during the COVID-19 pandemic: Vietnam experiences

This article aims to describe the Vietnamese Government?s policy response to support disadvantaged groups affected by the COVID-19 pandemic through the relief package. The Vietnamese policy response is characterized by rapid response, priority for disadvantaged groups in communities, social work core values in the policy, openness, and transparency. We hope the experiences from Vietnam could be helpful for similar settings in the world.

COVID-19-Related Intolerance of Uncertainty and Mental Health among Back-To-School Students in Wuhan: The Moderation Effect of Social Support.

The current wave and future trend of the novel coronavirus disease 2019 (COVID-19) has triggered public uncertainty, causing unbearable psychological pressure on people. A cross-sectional online questionnaire was conducted among back-to-school students in Wuhan from 31 August 2020, to 14 September 2020, by using convenience sampling. A total of 1017 participants voluntarily provided sociodemographic characteristics and accomplished the following scales: the Intolerance of Uncertainty Scale (IUS-12), the Social Support Scale (SSQ), the Generalized Anxiety Disorder Scale (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), and the Insomnia Severity Index-7 (ISI-7). Results revealed that the levels of anxiety, depression, and insomnia were moderate, moderate and subthreshold, respectively. A one-way multivariate analysis of variance indicated that those with different attitudes toward the trajectory of the COVID-19 epidemic in China showed significantly different results in anxiety and depression (p < 0.001). Moderation modeling implicated that social support significantly moderated the predictive relationship between intolerance of uncertainty and mental health variables including anxiety and depression, but failed on insomnia. Findings indicate that back-to-school students in Wuhan experience mental health problems and improving social support measures could buffer the effect of intolerance of uncertainty with respect to COVID-19 on mental health.

Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19.

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.

Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.

The Mediating Role of Internalized Stigma and Shame on the Relationship between COVID-19 Related Discrimination and Mental Health Outcomes among Back-to-School Students in Wuhan.

Outbreaks of an epidemic, such as coronavirus disease 2019 (COVID-19), always brings about far-ranging discrimination and stigmatization to the epicenter. This was a cross-sectional survey conducted to assess experienced discrimination, internalized stigma, shame, and mental health (anxiety, depression, distress, insomnia) among college students who merely had a perceived linkage with COVID-19, and explore the linkage between discrimination and negative mental health outcomes through the mediating effects of shame and internalized stigma. A total of 995 participants (53% female) were involved in this study, in which 40.9% of college students were reported to be discriminated against because of their experience in Wuhan. The experience of COVID-19-related discrimination is indirectly associated with anxiety, depression, and insomnia, in which shame and internalized stigma play a complete mediating effect. Meanwhile, it is both directly and indirectly associated with distress through shame and internalized stigma. The findings of this study suggest that COVID-19-related discrimination is associated with shame and internalized stigma, which in turn predict psychological symptoms over time.

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.