Prevalence and Selected Sociodemographic of Movement Behaviors in Schoolchildren from Low- and Middle-Income Families in Nanjing, China: A Cross-Sectional Questionnaire Survey
Bibliographic record
Abstract
BACKGROUND: To investigate the prevalence of meeting the 24-h movement guidelines and its sociodemographic correlates in youth from low- and middle-income families (LMIFs) in Nanjing, China. METHODS: Cross-sectional data on moderate to vigorous physical activity (MVPA), screen time (ST), and sleep (SLP) were collected using the Chinese version of Health Behavior School-Aged Children questionnaire among grade schoolchildren of 4th-12th (mean age 13.6 years). The prevalence of meeting the 24-h movement guidelines was in line with the recommendations of the Canadian 24-h movement guidelines. Generalized linear models were used to explore the relationships of correlates with the prevalence of meeting the movement guidelines. RESULTS: The prevalence of meeting the MVPA, ST, and SLP guidelines and their combination was 9.9%, 65.2%, 37.2%, and 2.9%, respectively. As for the prevalence of meeting the MVPA guidelines, boys, younger schoolchildren, and those living in urban areas had a higher chance of meeting them. With regards to the prevalence of meeting the ST guidelines, girls, urban schoolchildren, and the oldest participants were more likely to meet the guidelines. Residential areas and grades were two correlates of meeting the SLP guidelines as well as 24-h movement guidelines. CONCLUSIONS: The majority of schoolchildren from LMIFs in Nanjing, China had unhealthy movement behaviors. This concerning situation was mainly predicted by schoolchildren's grades, indicating older schoolchildren exhibited lower levels of movement behavior. Improved studies are encouraged to identify the correlates of movement behaviors in schoolchildren, which in turn designs and implements effective interventions.
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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".