Characterising urban green space density and footpath-accessibility in models of BMI
Bibliographic record
Abstract
BACKGROUND: While exposure to urban green spaces has been associated with various physical health benefits, the evidence linking these spaces to lower BMI, particularly among older people, is mixed. We ask whether footpath availability, generally unobserved in the existing literature, may mediate exposure to urban green space and help explain this volatility in results. The aim of this study is to add to the literature on the association between urban green space and BMI by considering alternative measures of urban green space that incorporate measures of footpath availability. METHODS: We conduct a cross-sectional study combining data from The Irish Longitudinal Study on Ageing and detailed land use information. We proxy respondents' exposure to urban green spaces at their residential addresses using street-side and area buffers that take account of the presence of footpaths. Generalised linear models are used to test the association between exposure to several measures of urban green space and BMI. RESULTS: Relative to the third quintile, exposure to the lowest quintile of urban green space, as measured within a 1600 m footpath-accessible network buffer, is associated with slightly higher BMI (marginal effect: 0.80; 95% CI: 0.16-1.44). The results, however, are not robust to small changes in how green space is measured and no statistically significant association between urban green spaces and BMI is found under other variants of our regression model. CONCLUSION: The relationship between urban green spaces and BMI among older adults is highly sensitive to the characterisation of local green space. Our results suggest that there are some unobserved factors other than footpath availability that mediate the relationship between urban green spaces and weight status.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".