Cross-National Comparisons of Time Trends in Overweight Inequality by Socioeconomic Status Among Women Using Repeated Cross-Sectional Surveys From 37 Developing Countries, 1989–2007
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chronic diseases are now among the leading causes of morbidity and mortality in lower income countries. Although traditionally related to higher individual socioeconomic status (SES) in these contexts, the associations between SES and chronic disease may be actively changing. Furthermore, country-level contextual factors, such as economic development and income inequality, may influence the distribution of chronic disease by SES as well as how this distribution has changed over time. Using overweight status as a health indicator, the authors studied repeated cross-sectional data from women aged 18-49 years in 37 developing countries to assess within-country trends in overweight inequalities by SES between 1989 and 2007 (n=405,550). Meta-regression was used to examine the associations between gross domestic product and disproportionate increases in overweight prevalence by SES, with additional testing for modification by country-level income inequality. In 27 of 37 countries, higher SES (vs. lower) was associated with higher gains in overweight prevalence; in the remaining 10 countries, lower SES (vs. higher) was associated with higher gains in overweight prevalence. Gross domestic product was positively related to faster increase in overweight prevalence among the lower wealth groups. Among countries with a higher gross domestic product, lower income inequality was associated with faster overweight growth among the poor.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it