Undeveloped green space and free-time physical activity in 11 to 13-year-old children
Bibliographic record
Abstract
BACKGROUND: Research on the association between the physical environment and physical activity in children has focused on built and developed features or total green space. The impact of natural, undeveloped green spaces is unknown. The objective of this study was to determine whether the presence of undeveloped green spaces in the home neighborhood are associated with physical activity in 11 to 13-year-olds. METHODS: This was a cross-sectional study of grade 6 to 8 urban residing Canadian students who participated in the 2009/10 Health Behaviour in School-Aged Children survey. Children self-reported the frequency they participated in physical activity in their free-time outside of school hours. Geographic Information Systems (GIS) were used to assess the proportion of land area within 1 km of participants' homes that was devoted to publicly accessible meadows (i.e., field vegetated primarily by grass and other non-woody plants) and treed areas (i.e., field vegetated primarily by trees and shrubs). Ordinal logistic regression models were used to examine the relationships between the undeveloped green space areas and free-time physical activity. Several intrapersonal, family, and neighborhood environment factors were controlled for in these regression models. RESULTS: The proportion of neighborhood land covered by meadows was not associated with the physical activity outcome (p > 0.6). However, the proportion of neighborhood land covered by treed areas was independently associated with the physical activity outcome (p = 0.02). For each additional 5% increase in the proportion of neighborhood land covered by treed areas there was a corresponding 5% increase (95% confidence interval: 1-10% increase) in the relative odds of increasing free-time physical activity outside of school hours. CONCLUSIONS: The physical activity levels of 11 to 13-year-old children was associated with the amount of space in their home neighborhood devoted to treed areas.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".