Your browser doesn't support javascript.
Women working in healthcare sector during COVID-19 in the National Capital Region of India: a case study
Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach ; : 93-119, 2022.
Article in English | Scopus | ID: covidwho-2035584
ABSTRACT
Healthcare workers are the backbone of society, serving and aiding people with biological illnesses. They are the most vulnerable part of society, as there is no work-from-home option available to the health sector workers during the pandemic. The study aims to investigate the issues faced by women working in the health care sector as nurses, doctors, etc., during the pandemic COVID19. The pandemic increased the workload of women as a whole and particularly women working in the health sector. The paper focuses on issues and challenges faced by women working in the healthcare sector during the pandemic, revealing the number of hours they had to work, how they got quarantined, the issues they faced while working, the challenge of stealing time to be spent with family and the nightmare of coping the truth that the virus might be transmitted to their children and other family members, the fear of working in coronavirus ward, the issues faced in wearing the double mask and personal protective equipment. It would also highlight the issues and struggle they had to go through while working in the healthcare sector as a woman. © 2022 Elsevier Inc. All rights reserved.
Keywords

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Case report Language: English Journal: Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Case report Language: English Journal: Artificial Intelligence, Machine Learning, and Mental Health in Pandemics: A Computational Approach Year: 2022 Document Type: Article