Food and Nutrition Insecurity and Farming Household Resilience to COVID-19 Shocks in Ghana
Bibliographic record
Abstract
The COVID-19 pandemic affected farming households in Sub-Saharan Africa. As a case in point, we examine farming households’ resilience to food and nutrition insecurity in Ghana under the COVID-19 shocks. Focus group discussions were initially conducted with farming household heads to identify households’ sources of resilience and the different COVID-19 shocks. A multi-stage random sampling technique was then used to survey 252 farming households. We used different econometric modelling techniques, that is, the multiple indicators multiple causes modelling procedure, ordinary least square, and multinomial probit model, for data analysis. Our results provide supportive evidence affirming that COVID-19 shocks undermine farming households’ resilience and food and nutrition security. Urban and male-headed households experience more food and nutrition insecurity than rural and female-headed households. Farming households’ adaptive capacity significantly contributed to household resilience and food and nutrition security. The findings suggest that lessons learned from the current pandemic can help policymakers, governments, and international organisations build adequate responses and interventions that strengthen and support farming households’ resilience to food and nutrition security and systemic shocks such as COVID-19 in Ghana.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".