Impact of COVID-19 Policy Measures on Food Security Among Urban Residents in Blantyre City, Malawi
Bibliographic record
Abstract
Abstract Background Malawi, a vulnerable country in Sub Saharan Africa (SSA) is at the helm of experiencing food insecurity amidst COVID-19 as the vast majority survives on the hand-to-mouth economy. However, knowledge about how COVID-19 policy measures lead to food insecurity among the urban residents in Malawi is scanty. Understanding this link is crucial for designing the interventions that can help reduce the risk of being food insecure while containing further spread of the virus. Using Bronfenbrenner’s ecological theory as a conceptual framework, we explore the impact of COVID-19 policy measures on food security experienced by Blantyre residents in Malawi. We interviewed fifteen participants composed of private secondary school teachers and informal workers to understand their experiences of food insecurity linked to COVID-19 policy measures in place by the Malawi government. Results Our results show that participants face difficulties to access adequate food and have also changed their eating habits by skipping meals in some days due to loss of jobs, underpayment as well as business disruption. Conclusion Based on the findings, we argue that the COVID-19 policies have aggravated severe challenges among urban residents to access adequate food rendering them food insecure. To ensure sustained livelihood, we suggest the Malawi government should design immediate interventions such as relief fund packages targeting the urban poor to rescue them from facing acute food shortages while containing the pandemic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".