The Associations of Built Environment with Older People Recreational Walking and Physical Activity in a Chinese Small-Scale City of Yiwu
Bibliographic record
Abstract
Physical activity would bring in plenty of health benefits, especially recreational physical activity (RPA). Previous studies have suggested that built environment would affect older people's recreational walking (RW) and RPA, but how the effects exist in a small-scale Chinese city remains unclear. Two hundred and fifty-two older participants were recruited in the city of Yiwu using cross-sectional survey of random samples in 2019. RW and RPA level of participants and perceived scores of built environments were collected using the International Physical Activity Questionnaire and Neighborhood Environment Walkability Scale, respectively. Linear regression analysis was conducted to investigate the association of built environment with older people's RW and RPA. The results showed that two main factors affecting older people's RW and RPA were residential density and aesthetics. Additionally, access to services was related to RW, and street connectivity was correlated with RPA. The associations of RW with built environment varied slightly with demographic variables included in the regression model. All the results suggested that lower residential density, better aesthetics environment, and higher street connectivity would motivate older people to engage more in RW and RPA. The better access to services encourages only RW, not RPA, in older people. These findings would be helpful for policy decision makers in the urban construction process in Yiwu. More studies are needed to enlarge the scientific evidence base about small-scale cities in China.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".