Urban Household Characteristics and Dietary Diversity
Bibliographic record
Abstract
BACKGROUND: The world's population is increasingly becoming urbanized. If the current urban growth rate is to continue, new and unprecedented challenges for food security will be inevitable. Dietary diversity has been used to ascertain food security status albeit at the multicountry and country levels. Thus, household-level studies in urban settings, particularly in sub-Sahara African, are few. Yet, it is imperative that assessments of food security are undertaken particularly in urban settings, due to the projected fast rate of urbanization and the challenges of attaining food security. OBJECTIVE: To examine household characteristics and dietary diversity. METHODS: The study uses data from 452 households from the second round of the Regional Institute for Population Studies (RIPS) EDULINK urban poverty and health study. Bivariate and multivariate analyses are undertaken. RESULTS: Mean dietary diversity for all households is 6.8. Vegetables have the highest diversity, followed by cereal-based and grain products. Household characteristics that have statistically significant associations with dietary diversity include sex and level of education of household head, household wealth quintile, and source of food. CONCLUSIONS: There is high dietary diversity in the study communities of Accra but low consumption of foods rich in micronutrient, such as fruits and milk/dairy products. The study brings to fore issues related to resource-disadvantaged entities of the urban system, namely, females, poor households, and the non-educated who have food insecurity problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".