Browsing by Author "John, SF"
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Item Examining the Covid-19 Coping Strategies Employed by Residents in Selected South Africa’s Rural Areas(SPCRD Global Publisihing, 2023-06) Okem, Andrew Emmanuel; MUBANGIZI, BETTY CLAIRE; Adekanla, N; John, SF; Ibrahima, BRural communities are vulnerable to shocks associated with the COVID-19 pandemic. The resilience of these communities depends on their ability to cope with the impacts of such shocks. This study examines the COVID-19 coping strategies of residents of Matatiele and Winnie Madikizela Mandela local municipalities in South Africa. We collected primary data through 11 FGDs and 13 individual interviews. Of the six coping strategies identified, the most cited was resorting to alternative food sources to address food insecurity. Other coping strategies include alternative sources of income; reducing remittance and expenditure; shifting to new activities; and introducing emotional support. The findings reveal that coping strategies entail changes around basic needs such as food and income. To protect these communities against future shocks, strong local institutions working in collaboration will be invaluable in empowering communities to identify and implement alternative livelihoods while building supportive infrastructure.Item Understanding the Interplay Among Vulnerabilities, Livelihoods, and Institutional Dynamics in the Context of COVID-19: A Case Study of Selected Rural Communities in South Africa(University of KwaZulu-Natal, 2023-07-15) Mubangizi, Betty Claire; Okem, Andrew Emmanuel; John, SF; Ngubane, Mnqobi; Barry, I; Adekanla, N; Nyawo, Jabulani ChristopherAbout the report While our academic outputs cater for scholarly audiences, offering intricate analyses and methodological deliberations, this technical report considers a different path. Recognising the vast spectrum of stakeholders; from policy makers to practitioners - whose decisions and actions are pivotal in shaping the future of rural communities, we’ve crafted this report to be accessible, clear, and action-oriented. Simplicity: Away from the academic jargon and complex terminologies, this report communicates in a plain language, ensuring that findings are clear despite the academic level. Visuals: To further enhance the report’s accessibility, we’ve incorporated a range of visuals – charts, graphs, and illustrative diagrams – to aid the understanding, and also offer a quick snapshot of key findings and recommendations. Utility: At its core, the report aims to be a tool for decision-makers, offering them grounded insights and actionable recommendations. By shedding light on the interplay of vulnerabilities, livelihoods, and institutional dynamics in the context of COVID-19 in rural settings, we hope to inform strategies that can more effectively address similar challenges in future. In addition, the qualitative data from this study was analysed using content analysis. The content analysis was carried out thematically by coding the bunch of text obtained from the field and creating themes as coding, thematic classification of coded texts, and integrating, refining and writing-up theoretical outcomes. NVIVO version 12 was used to code and develop themes from the data with thorough probing into the socio-economic implication of the pandemic on our unit of analysis. Specifically, well-defined explanatory mix, spread across various pre-existing resource endowment of individuals in the sustainable livelihood framework were used to analyse the socio-economic vulnerability of people using consumption and income effect of the pandemic. In conclusion, this technical report stands as a bridge between rigorous research and actionable insights. In synthesising our findings in a user-friendly format, we aspire to arm policymakers and practitioners with the knowledge and understanding required to devise robust, informed strategies. Strategies that can pave the way for more resilient rural communities, better equipped to face the multifaceted challenges of disasters and pandemics. The report is divided into two sections, presenting qualitative and quantitative data.