Rural–Urban Divide in the Prevalence and Correlates of Overweight and Obesity Among Women of Reproductive Age in Nigeria: A Multilevel Analysis of Repeated Cross-Sectional Data
Bibliographic record
Abstract
We examined rural and urban prevalence and correlates of overweight/obesity among women of reproductive age using survey data from Nigeria. Overweight and obesity prevalence increased from 16.1% and 6.1% in 2008 to 18.2% and 10.0% in 2018, while underweight prevalence consistently averaged at 12%. Regardless of the residential setting, age, marital status, education, occupation, wealth, and year were associated with higher risk of overweight/obesity, whereas breastfeeding showed a protective effect. Unique risk factors for overweight/obesity in urban areas were higher parity and female-headed households, while ethnicity, media exposure, and state of residence were unique risk factors in rural areas.
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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 | 0.000 |
| 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".