Neighborhood Built Environment Measures and Association with Physical Activity and Sedentary Time in 9–14-Year-Old Children in Saskatoon, Canada
Bibliographic record
Abstract
This study assessed whether perceptual and researcher-rated measures of neighborhood-built environments (BEs) predict device-based multiple activity-related outcomes, specifically: moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), and sedentary time (ST), in children. Eight hundred and sixteen children aged 9-14 years from Saskatoon, Canada, were surveyed on their perceptions of BE, and their PA outcomes were objectively monitored for one week at three different time frames over a one-year period, September 2014 to August 2015. The researcher-rated BE measures were collected by trained researchers using multiple BE audit tools: neighborhood active living potential (NALP) and Irvine Minnesota inventory (IMI), 2009-2010. A multilevel modeling approach was taken to understand BE influences of children's PA outcomes. Children's perceived availability of parks and sidewalks predicted a higher accumulation of MVPA and a lower accumulation of ST. Children's report of the absence of neighborhood social disorder (e.g., threats from scary dogs/people) predicted a higher LPA, while reported concern about crime predicted a lower MVPA. Researcher-rated neighborhood activity friendliness predicted a lower ST, however, researcher-rated safety from crime predicted a higher ST. Perceived BE characteristic were stronger predictors of children's PA outcomes compared to researcher-rated BE factors.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".