Comparison Study of Perceived Neighborhood-Built Environment and Elderly Leisure-Time Physical Activity between Hangzhou and Wenzhou, China
Bibliographic record
Abstract
Physical activity and health are of significant importance for the rapid aging population in China. Built environment has been suggested to be associated with elderly physical activity and health. However, the association differences between cities remain unclear. Perceived built environment scores and elderly leisure-time physical activity (LTPA) of 308 elderly in Hangzhou and 304 elderly in Wenzhou were collected using Neighborhood Environment Walkability Scale and International Physical Activity Questionnaire, respectively. A multivariate linear regression method and T-test were used to analyze of the associations between elderly LTPA and built environment and the differences between the two cities, respectively. The results showed that LTPA was positively associated with walking/cycling facilities and crime safety in both cities. LTPA was positively correlated with residential density, aesthetics, pedestrian/traffic safety in Wenzhou and negatively correlated with access to services in Hangzhou. The perceived scores of aesthetics (2.71 vs. 2.45) and pedestrian/traffic safety (2.11 vs. 1.71) in Hangzhou were significantly higher than those in Wenzhou. The results suggested that built environment elements like higher walking/cycling facilities and crime safety may motivate elderly engaging LTPA in both cities. However, LTPA was affected by different factors in these two cities. In the urban redevelopment, survey conducted in its own city would provide meaningful information and cannot be neglected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".