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Record W2103934552 · doi:10.1007/s12160-012-9433-y

Individual- and Area-Level Disparities in Access to the Road Network, Subway System and a Public Bicycle Share Program on the Island of Montreal, Canada

2013· article· en· W2103934552 on OpenAlexafffundabout
Daniel Fuller, Lise Gauvin, Yan Kestens

Bibliographic record

VenueAnnals of Behavioral Medicine · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversité de MontréalUniversity of SaskatchewanSaskatchewan Health
FundersCanadian Institutes of Health Research
KeywordsPublic healthPublic transportMultilevel modelGeographyPoison controlEnvironmental healthHealth psychologyPopulationBusinessSocioeconomicsEconomic growthTransport engineeringGerontologyMedicineEngineeringSociologyEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have examined potential disparities in access to transportation infrastructures, an important determinant of population health. PURPOSE: To examine individual- and area-level disparities in access to the road network, public transportation system, and a public bicycle share program in Montreal, Canada. METHODS: Examining associations between sociodemographic variables and access to the road network, public transportation system, and a public bicycle share program, 6,495 adult respondents (mean age, 48.7 years; 59.0 % female) nested in 33 areas were included in a multilevel analysis. RESULTS: Individuals with lower incomes lived significantly closer to public transportation and the bicycle share program. At the area level, the interaction between low-education and low-income neighborhoods showed that these areas were significantly closer to public transportation and the bicycle share program controlling for individual and urbanicity variables. CONCLUSIONS: More deprived areas of the Island of Montreal have better access to transportation infrastructure than less-deprived areas.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.032
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

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

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 head0.178
GPT teacher head0.369
Teacher spread0.191 · 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.

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

Citations16
Published2013
Admission routes3
Has abstractyes

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