Socioeconomic Discrepancies in Children’s Access to Physical Activity Facilities: Activity Space Analysis
Bibliographic record
Abstract
Few Canadian children meet current physical activity recommendations, especially children from households with lower socioeconomic status (SES). Previous work suggests that accessibility to, quality of, and cost of physical activity–promoting facilities influence physical activity levels. Disparities in accessibility to physical activity resources may contribute to neighborhood health and social inequalities. Many studies examine geographic accessibility to health-promoting facilities in residential neighborhoods and ignore individual mobility and other barriers to access such as cost and quality. This study examines SES differences in accessibility to physical activity facilities for schoolchildren as they move throughout the day. It does so by using activity spaces measured with a modified version of a road network buffer and a shortest-path network estimation method. SES-based differences in use and quality of visited physical activity resources are also considered. Results indicate that the high-SES sample has greater accessibility to physical activity facilities and uses them more frequently. Used facilities are of higher quality than those used by children living in low-SES neighborhoods. Cost is identified as a potential barrier to facility access for the low-SES group. To combat neighborhood health inequalities, cities should aim to provide high-quality, affordable, and accessible resources across all neighborhoods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".