Temporal trends in the physical fitness of Hong Kong children aged 6–12 years between 2003–04 and 2015–16
Bibliographic record
Abstract
Low physical fitness in childhood is linked with poor health now and in later life. This study estimated temporal trends in physical fitness for Hong Kong children aged 6-12 years from 2003-04 to 2015-16. Objectively measured body size and physical fitness data for 27,513 children were obtained from four population-representative surveys of Hong Kong primary school students. Temporal trends in means were estimated by population-weighted linear regression. Trends in distributional characteristics were visually described. Overall, there was a small increase in mean height (effect size (ES) = 0.20 (95%CI: 0.13, 0.28)), with a negligible increase (ES < 0.2) in mean body mass. When adjusted for trends in age, gender, and body size, there was a small decline in sit-and-reach performance (ES = -0.43 (95%CI: -0.43, -0.42)), a small improvement in 9-min run/walk performance (9-to-12-year-olds, ES = 0.26 (95%CI: 0.26, 0.27)), with negligible declines in handgrip strength, 6-min run/walk (6-to 8-year-olds), and sit-ups performance. Temporal trends were not always uniform across the population distribution, with declines in run/walk, sit-ups, and sit-and-reach performance generally largest in children with low fitness. Increased national health promotion strategies that address culturally specific factors are encouraged to further improve the existing trends, especially for children with low fitness.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".