The Effect of the Built Environment on Older Men′s and Women′s Leisure-Time Physical Activity in the Mid-Scale City of Jinhua, China
Bibliographic record
Abstract
Physical activity has been suggested to be beneficial in preventing disease and improving body function in older people. Older people's leisure-time physical activity (LTPA) is affected by various factors, especially environmental factors. However, the differences in the association between older people's LTPA and the built environment in different sex groups remain unclear. Perceived built environment scores and older people's LTPA were collected for 240 older people in Jinhua using the Neighborhood Environment Walkability Scale and International Physical Activity Questionnaire, respectively. A linear regression method was used to analyze the associations between older people's LTPA and the built environment in men, women, and all participants. The results showed that land use mix diversity was associated with LTPA in older people for both sexes. In men, LTPA was also associated with access to services. However, in women, LTPA was associated with residential density, street connectivity, and crime safety. The relationship varied when demographic variables were incorporated into the regression analysis. Those results indicated that a shorter perceived distance from home to destination would motivate older people to engage more in LTPA. Older people's LTPA was affected by various built environment factors according to different sex groups. Women's LTPA was generally more sensitive to the built environment. More studies are needed to confirm the association between LTPA in older people and the built environment in men and women in mid- or small-sized Chinese cities in the future.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".