Perceived implications of COVID-19 policy measures on food insecurity among urban residents in Blantyre Malawi
Bibliographic record
Abstract
BACKGROUND: Malawi is at the brink of experiencing food insecurity amidst the COVID-19 pandemic as the vast majority of its population lives in extreme poverty. While measures are being implemented to avert the spread of COVID-19, little is known about how COVID-19 policy measures have impacted food insecurity in urban Malawi. This study addresses this gap by exploring the implications of COVID-19 policy measures on food insecurity in low-income areas of Blantyre in Malawi. METHODS: We used Bronfenbrenner's ecological theory to explore the implications of COVID-19 policy measures on peoples' access to food. In-depth interviews were conducted with fifteen participants comprising of private school teachers, street vendors, sex workers, and minibus drivers. Data were analyzed using thematic analysis in which emerging patterns and themes from the transcripts were identified. RESULTS: The COVID-19 lockdown measures undermined participants' ability to maintain livelihoods. These measures have increased the vulnerability of the residents to food insecurity, forcing them to face severe challenges to accessing adequate food to support their families as a result of low incomes, job loss, and business disruptions. CONCLUSION: Our study underscores the need for the Malawi government to seriously consider the provision of basic necessities such as food to the urban poor. We also suggest that the Malawi government should continue and expand the social cash transfer or relief funding packages by targeting the most vulnerable groups in the city. There is also a need for the government to engage all stakeholders and work collaboratively with people at local level in policymaking decisions in times of crisis.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".