MétaCan
Menu
Back to cohort
Record W6921901070 · doi:10.11575/prism/33030

Interactions between Neighbourhood Urban Form and Socioeconomic Status and Their Associations with Anthropometric Measurements in Canadian Adults

2017· other· en· W6921901070 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersInstitute of Population and Public HealthInstitute of Musculoskeletal Health and ArthritisAlberta Cancer FoundationCanadian Institutes of Health ResearchAlberta InnovatesPublic Health AgencyPublic Health Agency of Canada
KeywordsSocioeconomic statusNeighbourhood (mathematics)DisadvantagedWaistMarital statusAnthropometryContext (archaeology)

Abstract

fetched live from OpenAlex

Neighbourhood-level socioeconomic composition and built context are correlates of weight-related behaviours. We investigated the relations between objective measures of neighbourhood design and socioeconomic status (SES) and their interaction, in relation to self-reported waist circumference (WC), waist-to-hip ratio, and body mass index (BMI) in a sample of Canadian adults ( from 12 Calgary neighbourhoods). WC and BMI were higher among residents of disadvantaged neighbourhoods, independent of neighbourhood design (grid, warped grid, and curvilinear street patterns) and individual-level characteristics (sex, age, education, income, dog ownership, marital status, number of dependents, motor vehicle access, smoking, sleep, mental health, physical health, and past attempts to modify bodyweight). The association between neighbourhood-level SES and WC was modified by neighbourhood design; WC was higher in disadvantaged-curvilinear neighbourhoods and lower in advantaged-grid neighbourhoods. Policies making less obesogenic neighbourhoods affordable to low socioeconomic households and that improve the supportiveness for behaviours leading to healthy weight in low socioeconomic neighbourhoods are necessary.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of CalgaryFrench-language works237,207