COVID‐19 pandemic, farming households' food and nutrition security, and response strategies in Ghana, West Africa
Bibliographic record
Abstract
Abstract We explored the association between COVID‐19 disruptions and food and nutrition security, including the various coping strategies adopted by farming households in Ghana. The different COVID‐19 shocks experienced and coping strategies implemented by farming households are identified through focus group discussions. A multistage random sampling method was used to survey 252 farming households, and data were analyzed using different regression techniques. We observe no significant differences in the food and nutrition security status of male‐ and female‐headed households, while the COVID‐19 disruptions affected male‐headed households more than female‐headed households. Our data shows a higher vulnerability of urban households to food and nutrition insecurity than rural households, with the COVID‐19 disruptions affecting urban households more than rural households. We find that the COVID‐19 disruptions pushed households to reduce their frequency of food consumption, consume less diverse diets, and hinder their adoption of coping strategies. Hence, responses that aim to strengthen farming households' frequency of consumption of essential food groups and access to nutritional and healthy diets are crucial to either help maintain or improve farming households' food and nutrition security during shocks such as COVID‐19 in Ghana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".