Trends in overweight prevalence among 11-, 13- and 15-year-olds in 25 countries in Europe, Canada and USA from 2002 to 2010
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to assess recent changes in the prevalence of overweight (including obesity) among 11-, 13- and 15-year-olds in 33 countries from 2002 to 2010. METHODS: Data from 25 countries from three consecutive survey cycles (2002, 2006 and 2010) that had at least 80% response rate for self-reported height, weight and age were analysed using logistic regression analysis. RESULTS: Overweight prevalence increased among boys in 13 countries and among girls in 12 countries; in 10 countries, predominantly in Eastern Europe, an increase was observed for both boys and girls. Stabilization in overweight rates was noted in the remaining countries; none of the countries exhibited a decrease over the 8-year period examined. In the majority of countries (20/25) there were no age differences in trends in overweight prevalence. CONCLUSION: In over half of the countries examined overweight prevalence did not change during 2002-2010. However, increasing overweight prevalence was noted in many Eastern European countries over this time period. Overweight prevalence remained high in several countries in Europe and North America. These patterns call for continued research in youth overweight and highlight the need to understand cross-national differences by examining macro-level indicators. Such research should feed into developing sound translations and practices to prevent and reduce overweight in youth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".