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Subpopulation differences in the association between neighborhood urban form and neighborhood-based physical activity

2014· article· en· W2043604743 on OpenAlexafffund
Gavin R. McCormack, Alan Shiell, Patricia K. Doyle–Baker, Christine M. Friedenreich, Beverly A. Sandalack

Bibliographic record

VenueHealth & Place · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsWalkabilityEnvironmental healthBuilt environmentPhysical activityAssociation (psychology)GerontologyDemographyLevel designPopulationLow incomeGeographyPsychologyMedicineSocioeconomicsSociologyPhysical therapyComputer science

Abstract

fetched live from OpenAlex

This study investigated whether associations between the neighborhood built environment and neighborhood-based physical activity (PA) varied by sociodemographic and health-related characteristics. A random sample of adults (n=2006) completed telephone- and self-administered questionnaires. Questionnaires captured PA, sociodemographic, and health-related characteristics. Neighborhood-based PA (MET-minutes/week) was compared across low, medium, and high walkable neighborhoods for each sociodemographic (sex, age, dependents, education, income, motor vehicle access, and dog ownership) and health-status (general health and weight status) subpopulation. With few exceptions, subpopulations residing in high walkable neighborhoods undertook more (p<0.05) neighborhood-based PA than their counterparts in less walkable neighborhoods. Improving neighborhood walkability is a potentially effective population health intervention for increasing neighborhood-based PA.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.323
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations49
Published2014
Admission routes2
Has abstractno

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