Protection of Children in Difficulty in China during the COVID-19 Pandemic

COVID-19 and Vulnerable Children Well-Being: Interview with Left-Behind Children in Rural China

Purpose of the study: This study sought to explore the psychological well-being, academic adjustment, and quality of parental attachment of LBC during COVID-19 based on Left-Behind Children’s (LBC) word of mouth. Method: In light of the abundance of quantitative studies, this qualitative study explored the psychological, academic, and parental attachment experiences of rural LBC during COVID-19. To this end, we conducted semi-structured interviews with 22 LBCs aged 10–15 in May 2021. Result: The study results reveal that LBCs developed severe psychological illnesses after the pandemic severely disrupted their already disturbed lives. Our findings showed that most LBCs feel they do not need their parents, which reflects their long record of hopelessness and agony regarding the absence of their parents. Conclusion: Although COVID-19 is a global problem, its impact was particularly devastating for LBCs who have nobody around who could help them with their academic, personal and social need. In the modern COVID-19 era, it seems that Chinese grandparenting has become unreliable in the face of radical change in contemporary Education, society, and the economic system. Therefore, this study suggests that the Chinese government should seek to identify and monitor these children by working with NGOs that target such children.

Characterizing the Dynamic Evolution of Interagency Collaborative Decision-Making Networks in Response to COVID-19 in China: A Policy Document Analysis

Collaborative decision-making across multiple government agencies is considered a critical and effective strategy to combat public health crisis; however, we know little about how the collaborative decision-making works and evolves during periods of crisis. To fill this lacuna, this study uncovers the structure and evolving dynamics of the network by employing a policy document analysis. Based on the policy documents, jointly issued by the agencies of Chinese central government in four phases regarding COVID-19 control, we first constructed a co-occurrence matrix of policy-issuing agencies to outline the network structure, then drew a breadth–depth matrix to identify the role evolution of agencies, and lastly built a two-mode network consisting of policy topics and agencies to determine the evolution mechanisms of policy attentions for each agency. It was found that the network structure of interagency collaboration involves three forms: discrete structure in the early phase, subgroup structure in the middle phase, and connected structure in the latter phase. Agencies embedded in the network can be categorized into three types: leading agencies, key agencies, and auxiliary agencies, with their constituent members changed as the pandemic risks are gradually becoming under control. Furthermore, each type has its own primary policy attentions, but shares some common foci in all four phases and shifts attention in the emergency management process. This study contributes to shedding light on the formation of and variations in collaborative networks in health emergencies and provides policy implications for other countries that have struggled against COVID-19.

Word of Mouth from Left-Behind Children in Rural China: Exploring Their Psychological, Academic and Physical Well-being During COVID-19

COVID-19 has had considerable effects on people's lives worldwide, particularly left-behind children in China as they tragically witnessed the outbreak. From the outset, millions of left-behind children in rural areas experienced extensive physical and psychological disturbances because their migrant parents who lived in the city or another province could not be with them. This study explored the psychological, academic, and physical well-being of rural left-behind children during COVID-19. We captured the experiences of 10- to 15-year-old children and adolescents who were left behind by their migrant parents before the COVID-19 outbreak. The results of the present study suggest that left-behind children have exhibited severe psychological illnesses since the pandemic has limited their social interactions with peers and increased improper caretaking from grandparents.

Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.