Older adults’ outdoor walking and the built environment: does income matter?
Bibliographic record
Abstract
BACKGROUND: Our aim was to examine the association between Street Smart Walk Score® and self-reported outdoor walking among older Canadians, and to determine whether socioeconomic status modifies this association. METHODS: We linked objective walkability data with cross-sectional survey data from the Canadian Community Health Survey Healthy-Aging 2008-2009 Cycle for a sample of 1309 British Columbians aged ≥ 65 years. We examined associations between Street Smart Walk Score and meeting physical activity guidelines (≥150 min of moderate to vigorous activity/week) through self-reported outdoor walking using multivariable logistic regression, and tested for significant interactions with household income. RESULTS: A ten point higher Street Smart Walk Score was associated with a 17 % higher odds of meeting physical activity guidelines through walking outside (95 % CI: 1.07,1.27). In addition, older adults living in neighbourhoods categorised as Walker's Paradise were over three times more likely to meet guidelines than those living in Car-dependent/Very car dependent neighbourhoods. We found no evidence that household income moderated the effect of Walk Score on walking outside. CONCLUSIONS: Neighbourhood design may be one avenue whereby physical activity levels of older people can be enhanced through outdoor walking, with benefit across socioeconomic strata.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".