Examining the COVID-19 Coping Strategies Employed by Residents in selected South Africa’s rural areas
Bibliographic record
Abstract
Rural 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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".