Home gardening for advancing nutritional security and income generation in response to the COVID-19 pandemic in Nigeria
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic exacerbated food insecurity in Nigeria, particularly among vulnerable households. Home gardening emerged as a potential strategy to improve access to nutritious foods and support incomes. This study examines the impact of home gardening on household nutritional security using cross-sectional survey data from Nigerian households. Methods Nutritional security is proxied by the Household Dietary Diversity Score (HDDS), a validated indicator that reflects the variety of food groups consumed over a reference period. To address sample selection bias—where households that engage in home gardening may systematically differ from non-participating households—the study applies a Heckpoisson regression model. This approach accounts for endogenous selection into home gardening and enables the consistent estimation of its impact on dietary diversity. The analysis also incorporates key household demographic and socio-economic variables, including marital status, household size, and age composition. Results The study shows that income derived from home gardening is significantly associated with higher dietary diversity, indicating that home gardening enhances access to nutrient-rich foods and supports improved nutritional outcomes. In addition, marital status, household size, and the presence of younger and older household members significantly influence dietary patterns, highlighting the role of socio-demographic factors. Conclusions The findings underscore the importance of promoting home gardening as a viable strategy to improve dietary diversity and strengthen household nutritional security, particularly during periods of economic and food supply shocks. Policymakers should consider incorporating home gardening initiatives into broader food security and livelihood programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".