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Wave 1 results of the INTerventions, Research, and Action in Cities Team (INTERACT) cohort study: Examining spatio-temporal measures for urban environments and health

2021· article· en· W3187907429 on OpenAlexafffundabout
Daniel Fuller, Scott Bell, Caislin L. Firth, Nazeem Muhajarine, Trisalyn Nelson, Kevin G. Stanley, Meridith Sones, Jefferson M. Smith, Benoît Thierry, Karen Laberee, Zoé Poirier Stephens, Kole Phillips, Yan Kestens, Meghan Winters

Bibliographic record

VenueHealth & Place · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsSimon Fraser UniversityUniversity of SaskatchewanCentre Hospitalier de l’Université de MontréalMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionSocial connectednessBuilt environmentCohortGeographyCensusPopulationPsychologyEnvironmental healthApplied psychologyGerontologyMedicineSocial psychologyEngineeringNursing

Abstract

fetched live from OpenAlex

Built environment interventions have the potential to improve population health and reduce health inequities. The objective of this paper is to present the first wave of the INTErventions, Research, and Action in Cities Team (INTERACT) cohort studies in Victoria, Vancouver, Saskatoon, and Montreal, Canada. We examine how our cohorts compared to Canadian census data and present summary data for our outcomes of interest (physical activity, well-being, and social connectedness). We also compare location data and activity spaces from survey data, research-grade GPS and accelerometer devices, and a smartphone app, and compile measures of proximity to select built environment interventions.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.759
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.270
GPT teacher head0.448
Teacher spread0.177 · 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

Citations18
Published2021
Admission routes3
Has abstractyes

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