Exploring the Lived Experiences of Vulnerable Females from a Low-Resource Setting during the COVID-19 Pandemic.
Firoza HaffejeeRivesh MaharajhMaureen Nokuthula SibiyaPublished in: International journal of environmental research and public health (2023)
The onset of the Coronavirus disease 2019 (COVID-19) pandemic has affected the mental health and well-being of women in vulnerable settings. Currently, there is limited evidence that explores the wellness of elderly women under the associated restrictions. This study explores the lived experiences of elderly women in a vulnerable community in Durban, South Africa. A face-to-face, in-depth qualitative approach was implemented to interview 12 women aged 50 years and over. Thematic analysis was used to analyse the data. The findings suggest that social interactions, the effect of a high death rate, and financial strain predominantly affect stress and anxiety levels. Despite the women being in receipt of pensions and/or other grants, their supplementary income was reduced. This, together with the additional expenses incurred during the lockdown, resulted in anxiety over finances. The lack of social interaction, with limits on visiting family and other loved ones when they were ill, along with the limit on the number of people attending the funerals of loved ones were also stressful. This study also reports on the resulting coping mechanisms, which included using hobbies such as baking and sewing as a means of self-care. Religious beliefs also relieved stress while home remedies were used as preventative measures during the lockdown restrictions due to COVID-19.
Keyphrases
- mental health
- polycystic ovary syndrome
- coronavirus disease
- south africa
- healthcare
- pregnancy outcomes
- cervical cancer screening
- systematic review
- sars cov
- mental illness
- metabolic syndrome
- emergency department
- depressive symptoms
- type diabetes
- insulin resistance
- adipose tissue
- machine learning
- young adults
- men who have sex with men
- heat stress
- skeletal muscle
- deep learning
- tertiary care