Within-country migration and obesity dynamics: analysis of 94,783 women from the Peruvian demographic and health surveys
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
BACKGROUND: Rural-to-urban migration is associated with increased obesity, yet it remains unknown whether this association exist, and to what extent, with other types of internal migration. METHODS: We conducted a secondary analysis of the Peruvian Demographic and Health Surveys (2005 to 2012) on data collected from women aged 15-49 years. Participants were classified as rural stayers, urban stayers, rural-to-urban migrants, intra-rural migrants, intra-urban migrants, and urban-to-rural migrants. Marginal effects from a logit regression model were used to assess the probabilities of being and becoming obese given both the length of time in current place of residence and women's migration status. RESULTS: Analysis of cross-sectional survey data generated between 2005 and 2012. Data from 94,783 participants was analyzed. Intra-urban migrants and rural-to-urban migrants had the highest rates of obesity (21% in 2012). A steady increase in obesity is observed across all migration statuses. Relative to rural non-migrants, participants exposed to urban environments had greater odds, two- to three-fold higher, of obesity. The intra-rural migrant group also shows higher odds relative to rural stayers (42% higher obesity odds). The length of exposure to urban settings shows a steady effect over time. CONCLUSION: Both exposure to urban environments and migration are associated with higher odds of obesity. Expanding the characterization of within-country migration dynamics provides a better insight into the relationship between duration of exposure to urban settings and obesity.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it