Using Linked Data to Explore the Relationship Between Walking-Friendliness of Neighbourhoods, Physical Activity and Body Mass
Bibliographic record
Abstract
ABSTRACTObjectivesTo demonstrate the methodology and results for linking measures of neighbourhood walking-friendliness or "walkability" to Canadian health surveys and Canadian health surveys linked to administrative health care records. ApproachWe linked multiple measures of neighbourhood walkability to hundreds of thousands of 6-digit postal codes of respondents to three large Canadian surveys using geographic information systems and anonymized banks of postal codes. ResultsLong term exposure to walkable neighbourhoods was associated with increased reporting of walking for utilitarian purposes. Moving to a high walkable neighbourhood from a low walkable neighbourhood was associated with a full unit decrease in the body mass index of Canadian men over a 12-year follow-up. In a linkage of walkability measures to respondents who wore biosensors for a one-week period in several Canadian cities, we found that neighbourhood walkability was associated with increased reporting of utilitarian walking but not overall physical activity and step counts as measured by biosensors. ConclusionThere is potential for walkable neighbourhoods to influence physical activity and body weight of Canadians which is more evident when individuals are followed for long periods of time.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".