Does Urbanisation Matter? A Temporal Analysis of the Socio‐demographic Gradient in the Rising Adulthood Overweight Epidemic in China, 1989–2009
Bibliographic record
Abstract
Abstract Using eight successive waves of the China Health and Nutrition Survey, this study applies hierarchical age–period–cohort models to investigate the rising prevalence rates of adulthood overweight in China. We find that overweight prevalence rates increase throughout adulthood until the early 50s. Period effects are very strong with a virtually monotonic increase from 1989 to 2009. Yet, as posited by the reversal hypothesis (a diminishing positive association between overweight prevalence and socioeconomic status alongside development), this increase is levelling off or absent in most recent survey waves for women, urban residents, and individuals with higher educational attainment. Most importantly, substantial period variations are explained by rapid urbanisation, and the period increases in overweight prevalence closely track the pace of urbanisation in China. Cohorts born in the beginning years of the Great Chinese Famine (1958–1961) have the highest overweight prevalence rates, whereas cohorts experiencing the Famine during the childhood ages of adiposity rebound (the next rise in body mass index after infancy, typically from age 5 to 7 years) are significantly less likely to be overweight. These cohort patterns lend support to the critical‐period hypothesis. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".