An examination of the impact of neighbourhood walking environments on the likelihood of residents of dense urban areas becoming overweight or obese
Bibliographic record
Abstract
With the development of urbanization in China, obesity is becoming a serious problem, and the relationship between walking environments and obesity has attracted considerable interest. Using data from questionnaires (n = 418) gathered in 2017 from eight neighbourhoods in Guangzhou, China, a typical high‐density city, this study developed an Ordered Logit Model (OLM) to explore the effects of walking environments on the likelihood of residents becoming overweight or obese. The results demonstrate that body mass index (BMI) of individuals living in central urban areas is higher than those of suburban residents. After controlling for the effects of socio‐economic factors, it was found that the impact of walking environments at the scale of 1‐km buffer on individual BMI is the most significant. Variables of walkability, road network density, bus stop density, metro stop density, green coverage rate, and distance to the park have negative effects on BMI. Based on these findings, it is suggested that planning interventions should focus more on the areas through which residents walk in their daily travel routines. The selection of neighbourhoods surveyed and the sample size limit this study, but the conclusions do provide a scientific basis for the construction of neighbourhoods that encourage walking and decrease the probability of becoming overweight or obese.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".