Unpacking the reported impacts of COVID-19 in rural contexts: evidence from two rural municipalities in South Africa
Bibliographic record
Abstract
The COVID-19 pandemic, which has been primarily analysed within urban contexts, has revealed a significant gap in understanding its ramifications within rural areas. This study aims to address this gap by drawing upon the principles of Resilience Theory to shed light on the repercussions of the pandemic in rural contexts, thereby highlighting an overlooked aspect. This research employs a qualitative paradigm grounded in primary data collected from two rural municipalities, Matatiele and Winnie Madikizela Mandela Local Municipalities, in South Africa’s Eastern Cape Province. The primary data is derived from 11 focus group interviews involving essential stakeholders and 13 individual interviews that were purposefully selected. The study unravels a spectrum of adverse impacts from the COVID-19 pandemic across the two municipalities by thematically transcribing and coding audio records of interviews and focus groups using NVivo, employing inductive and deductive coding approaches. These impacts include employment loss, bereavement, food insecurity, and an upsurge in reported cases of gender-based violence. The pandemic’s ripples extend to cultural practices, education, and community well-being, as evidenced by the waning participation in cultural traditions, declining interest in education among school-goers, and a surge in substance abuse and criminal activities like stock theft. In light of these findings, the study advocates for customised pandemic responses in rural areas underpinned by the Resilience Theory framework. This entails endorsing enhanced healthcare infrastructure, fostering community-driven surveillance, promoting diversified livelihood strategies, and bolstering local governance structures. As the study expands our understanding, it concurrently underscores the significance of further exploration. The proposed avenue of research lies in examining how collaborative efforts among rural-based institutions can augment community resilience against the dual challenges of pandemics and disasters. Through this lens, the study emphasises the imperative of building adaptive capacity within rural communities, emphasising their ability to navigate uncertainties and emerge stronger in adversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".