The Frontlines and Margins: Gendered Care and Covid‐19 in the Indian Media

Among the many stories that emerged out of India during the pandemic, one was somewhat buried under the media discourse around the migrant crisis, lockdown regulations, and economic fallout. This was the story of striking accredited social health activist workers asking for fair wages, improved benefits, and better working conditions. The Covid‐19 crisis highlighted the poor health infrastructure and the precarious, and often, stigmatized nature of frontline work, managed at the community level by paramedical workers, a significant proportion of whom are women. There has been considerable attention paid by feminist groups as well as health‐related civil society organizations on the gender‐based inequities that have emerged during the pandemic, particularly in relation to care work. This study explores how care work performed by the accredited social health activists was framed in the mainstream media, through an examination of articles in three selected English daily newspapers over one year of the pandemic. Drawing on theoretical work deriving from similar health crises in other regions of the world, we explore how the public health infrastructure depends on the invisible care‐giving labor of women in official and unofficial capacities to respond to the situation. The systemic reliance on women's unpaid or ill‐paid labor at the grassroots level is belied by the fact that women's concerns and contributions are rarely visible in issues of policy and public administration. Our study found that this invisibility extended to media coverage as well. Our analysis offers a "political economy of caregiving” that reiterates the need for women's work to be recognized at all levels of functioning. © 2023 by the author(s);licensee Cogitatio (Lisbon, Portugal).

A Review on the Impact of Covid-19 on Mental Health and Wellbeing of Health Care Workers

The front-line health care workers faced many challenges and risks during this COVID-19 pandemic. The HCWs has a direct effect and carried a major burden and consequences in the control of this virus. Apart from physical stress the HCW suffering from psychological complications. This systemic review highlights the adverse mental health outcomes and other identifiable risk factors that affect their psychological behaviour during this COVID-19 pandemic. In this review, three databases were reviewed in different time points and literature have done according to WHO guidelines and PRISMA guidelines. In this review, we included various observational, experimental, and published articles that reported the mental health or psychological affects of the COVID-19 pandemic on HCWs. This study indicates that the COVID 19 pandemic has a potential effect on front-line HCWs in their psychological well-being. The data obtained from 24 studies in this review mainly from HCWs working at urban hospitals in China. Till now there is no evidence comparison with primary care workers. Whereas nurses are at high risk of adverse mental health outcomes compared to other health care workers. Other factors like gender, socioeconomic factors, underlying illness, lack of systemic support were the risk factors of adverse mental health outcomes. Furthermore, it is evident that PPE, exposure, workplace setting, testing have an impact on HCWs with COVID 19 infection and affect their mental health outcomes. It was observed that the maximum number of HCWs reported this COVID 19 infection during the first six months of the pandemic. The prevalence of hospitalization is 15% and with psychological problems of 1.5%. Still, extensive data is needed to observe the mental health problems among HCWs.

An Unsupervised Clustering Algorithm to Cluster the New SARS-CoV-2 Virus Mutation

The Coronavirus disease 2019 SARS-CoV-2 is a disease which causes fear to human lives that has taken thousands and hundreds of lives globally. The pandemic which has resulted in a global health emergency is currently a much sought-after research topic. The frequently mutating virus which has originated from Chiroptera and subsequently got transmitted to other mammals including humans. However, at the genomic level, it is yet to be unraveled what makes humans more prone to getting infected by the coronaviruses. Here, we have implemented a Machine Learning model known as K-means Clustering that uses the combination of different features to determine the risk of infection. In this research paper, the K-means clustering method is used since it is a good performer for Clustering analysis. The algorithm can group the sequences of the dataset into five clusters based on the Elbow plot and co-linearity of co-efficient. Using dimensional reduction technique PCA is used with a 3D visualization and a heat map to showcase the correlation efficiency between the mutated and original sequence considered. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.