The proportion of youths’ physical inactivity attributable to neighbourhood built environment features
Bibliographic record
Abstract
OBJECTIVE: We investigated the independent association between several neighbourhood built environment features and physical inactivity within a national sample of Canadian youth, and estimated the proportion of inactivity within the population that was attributable to these built environment features. METHODS: This was a cross-sectional study of 6626 youth aged 11-15 years from 272 schools across Canada. Participants resided within 1 km of their school. Walkability, outdoor play areas (parks, wooded areas, yards at home, cul-de-sacs on roads), recreation facilities, and aesthetics were measured objectively within each school neighbourhood using geographic information systems. Physical inactivity (<5 days/week of 60 minutes of moderate-to-vigorous physical activity) was assessed by questionnaire. Multilevel logistic regression analyses, which controlled for several covariates, examined relationships between built environment features and physical inactivity. RESULTS: The final regression model indicated that, by comparison to youth living in the least walkable neighbourhoods, the risks for physical inactivity were 28-44% higher for youth living in neighbourhoods in the remaining three walkability quartiles. By comparison to youth living in neighbourhoods with the highest density of cul-de-sacs, risks for physical inactivity were 28-32% higher for youth living in neighbourhoods in the lowest two quartiles. By comparison to youth living in neighbourhoods with the least amount of park space, risks for physical inactivity were 28-37% higher for youth living in the neighbourhoods with a moderate to high (quartiles 2 and 3) park space. Population attributable risk estimates suggested that 23% of physical inactivity within the population was attributable to living in walkable neighbourhoods, 16% was attributable to living in neighbourhoods with a low density of cul-de-sacs, and 15% was attributable to living in neighbourhoods with a moderate to high amount of park space. CONCLUSIONS: Of the neighbourhood built environment exposure variables measured in this study, the three that were the most highly associated with inactivity were walkability, the density of cul-de-sacs, and park space. The association between some of these features and youths' activity levels were in the opposite direction to what has previously been reported in adults and younger children.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".