Differences of height and body mass index of youths in urban vs rural areas in Hunan province of China
Bibliographic record
Abstract
BACKGROUND: Economic reforms in China were implemented approximately 30 years ago. Since then, people's nutrition, living conditions and overall health have continually improved, but there has been an imbalance between the progresses in urban vs rural areas. Height and body mass index (BMI) are regarded as two important indicators of nutritional status and overall health. AIM: The aim of this study was to investigate differences in height and BMI between Chinese youths of rural vs urban areas and further, to determine whether these differences have changed over time (1990s vs 2000s). SUBJECT AND METHODS: 24 194 urban youths and 7130 rural youths were recruited in Hunan province of China. In each gender group, the subjects were divided into eight subsets according to age, geographic area residence, and decade when the youths were measured. Independent t-tests were used to test the differences of height and BMI between the studied groups. RESULTS: Both male and female youths from urban areas were significantly taller than youths from rural areas in both the 1990s and 2000s (all p<0.001), with the exception of the 1990s female 15-18 years subset (p=0.21). The height of youths was significantly greater in the 2000s compared to the corresponding gender and geographic subset in the 1990s (p<0.001), except for the female 15-18 years subset from rural areas (p=0.10). Similar results were obtained for BMI. CONCLUSION: There are significant differences in height and BMI between youths raised in urban vs rural areas, and positive growth trends of height and BMI over time (1990s vs 2000s) in youths in Hunan Province of China.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".