Dog-walking in dense compact areas: The role of neighbourhood built environment
Bibliographic record
Abstract
There is a dearth of evidence about how high-density living may influence dog-walking behaviour. We examined associations between neighbourhood built environment attributes and dog-ownership and dog-walking behaviour in Japan. Data from 1058 participants were used. The dog-ownership was 18.8%. All neighbourhood built attributes (excluding availability of parks) were negatively associated with dog-ownership. Among dog-owners, these same attributes were positively associated with any dog-walking in a usual week and with achieving 150-min per week of physical activity through dog-walking alone. These findings provided evidence on the importance of neighbourhood built environment attributes on dog-ownership and dog-walking behaviour in dense and compact areas. The urban design and public health implication of these findings is that the built environment attributes in high-density living areas may have different impacts on dog-ownership and dog-walking: while living in a walkable neighbourhood may not be conducive to dog-ownership, it may support dog-walking in such areas. Programs targeting dog-owners in high-density areas might be needed to encourage them to walk their dogs more. If successful, these programs could contribute to higher physical activity levels among dog-owners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".