The contributions of neighbourhood design in promoting metabolic health
Bibliographic record
Abstract
Abstract The design and quality of the neighbourhood built environment can encourage health-supportive behaviours and support cardiometabolic health. However, despite the relationships between demographic and behavioural risk factors of metabolic syndrome being investigated by many studies, only some studies have directly estimated the associations between the built environment and metabolic syndrome. Using data from Canada, we examined the associations between the neighbourhood built environment and metabolic syndrome. Data from Alberta’s Tomorrow Project participants, conducted in Alberta, Canada, was used (n = 6718). Metabolic syndrome was defined as the presence of at least three clinical risk factors among lipid levels, blood pressure, and waist circumference. The normalised difference vegetation index was used to quantify the greenness of each participant’s neighbourhood. Built attributes of participants’ neighbourhoods associated with supporting physical activity, including dwelling density, intersection density, and the number of points of interest, were obtained via the Canadian Urban Environmental Health Research Consortium. Increases in the number of points of interest and total active living environment-friendliness of the neighbourhood were associated with having fewer metabolic syndrome risk factors (b = −0.11, 95% CI −0.16, −0.07 and b = −0.03, 95% CI −0.05, −0.01, respectively) and lower odds of metabolic syndrome (OR = 0.89, 95% CI 0.84, 0.094 and OR = 0.97, 95% CI 0.95, 0.99, respectively). Furthermore, higher dwelling density was associated with having fewer metabolic syndrome risk factors (b = −0.05, 95% CI −0.09, −0.01). Our findings highlight the importance of urban design to prevent and potentially manage metabolic syndrome and improve population health.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".