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Record W2607227275 · doi:10.23889/ijpds.v1i1.343

Using Linked Data to Explore the Relationship Between Walking-Friendliness of Neighbourhoods, Physical Activity and Body Mass

2017· article· en· W2607227275 on OpenAlexaffabout
Nancy A. Ross, Kaberi Dasgupta, Claudia Sanmartin, Rania Wasfi, Samantha Hajna, Sarah M Mah

Bibliographic record

VenueInternational Journal for Population Data Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsStatistics CanadaMcGill University Health CentreMcGill University
Fundersnot available
KeywordsWalkabilityNeighbourhood (mathematics)Physical activityEnvironmental healthGeographyBuilt environmentLevel designGerontologyMedicinePsychologyEcologyComputer sciencePhysical therapy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.006
Open science0.0050.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.472
GPT teacher head0.520
Teacher spread0.048 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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