Associations between neighbourhood built characteristics and sedentary behaviours among Canadian men and women: findings from Alberta's Tomorrow Project
Bibliographic record
Abstract
Evidence of associations between neighbourhood built characteristics and sedentary behaviours is mixed. The study aim was to investigate the associations between objectively-derived neighbourhood built characteristics and self-reported sedentary behaviours among Canadian men and women. This study sourced survey data from Alberta's Tomorrow Project (2008; n = 14,785), in which sitting and motor vehicle travel times during the last 7 days was measured. Geographic Information System was used to calculate neighbourhood built characteristics within a 400 m buffer of participant's home and a walkability score was estimated. To estimate the associations between neighbourhood characteristics and sedentary behaviours, covariate-adjusted generalized linear regression models were used. Walkability, 3-way intersections, and population count were positively associated with sitting time. Business destinations and greenness were negatively associated with sitting time. Walkability, 3-way, and 4-way intersections were negatively associated with motor vehicle travel time. Sex-specific associations between neighbourhood characteristics and sedentary behaviour were found. Among men, business destinations were negatively associated with sitting time, and 3-way intersections, population count, and walkability were negatively associated with motor vehicle travel time. Among women, Normalized Difference Vegetation Index was negatively associated with sitting time. Interventions to reduce sedentary behaviours may need to target neighbourhoods that have built characteristics which might support these behaviours. More research is needed to disentangle the complex relationships between different neighbourhood built characteristics and specific types of sedentary behaviour.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".