Assessment of the Effect of Covid-19 Pandemic Lockdown Dietary Diversity among Urban Households in Jos, Plateau State, Nigeria
Bibliographic record
Abstract
Dietary diversity became a global concern in improving health conditions through the habit of food group consumption by adding health dimension to the issue of food calorie consumption. Access to nutritionally adequate and good quality diet is essential to human health, productivity and work output. However, despite the various concerns by governments all over the world on ensuring that every household can at least provide three square meals per day, food insecurity continues to be a major development problem across the globe. This study assessed the effect of COVID-19 pandemic lockdown on households’ dietary diversity in Jos Metropolis, Plateau State, Nigeria. A multistage sampling technique was used to select 265 households. Data was collected using well-structured questionnaire. The analytical techniques were; Descriptive statistics, Dietary Diversity index and Z-Statistics Test. Gender, age, household size, education, marital status, cooperative membership and access to credit were the socioeconomic characteristics described. Result indicate that 86% of the households had low food dietary diversity while 14% of the households had high food dietary diversity before and after the pandemic lockdown. Similarly, 18% of the households had low calorie consumption while 82% of the households had high dietary diversity before and after the pandemic lockdown. Cereals, legumes/grains, oils/fat, roots and tubers, sugar and honey and meats were the most common food consumed by the households daily before and after the COVID-19 pandemic lockdown. Result further indicate that the pandemic lockdown had effect on the dietary diversity and food consumption patterns of the urban households. It can be concluded that the understanding of the effect of the COVID-19 pandemic lockdown on dietary diversity and food consumption patterns of households is important in developing policy measures such as social safety nets, home feeding programmes, the school feeding programme, conditional cash transfers schemes and improved marketing channels that will help mitigate against households falling into food insecurity during similar pandemic in the future.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".