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Record W4390187210 · doi:10.32866/001c.91402

Linking Neighborhood Walkability to the Independence and Quality of Life of Older Adults across Canada

2023· article· en· W4390187210 on OpenAlexafffundabout
Paul Redelmeier, Meredith Alousi-Jones, Merrina Zhang, Isabella Jimenez, Ahmed El-Geneidy

Bibliographic record

VenueFindings · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsNational Research Council CanadaMcGill University
FundersNational Research Council CanadaGovernment of Canada
KeywordsWalkabilityIndependence (probability theory)PerceptionQuality of life (healthcare)GerontologyDestinationsPsychologyBuilt environmentEnvironmental healthGeographyMedicineEngineering

Abstract

fetched live from OpenAlex

In car dependent societies, driving cessation may reduce older adults’ independence and quality of life. One way to maintain independence for older adults after quitting driving is to encourage walking to local destinations. This paper explores how neighborhood walkability impacts older adults’ ability to maintain their lifestyles as they age. Based on data collected from the 2023 Aging in Place survey (N=3,551), we analyze the relationship between survey respondents’ perceptions of transport in their neighborhood and its Walk Score across 6 Canadian regions. We explore the association between neighborhood walkability and respondents’ perception of their independence, quality of life, and likelihood of needing to move in the future. We find that those living in walkable neighborhoods believe that they will maintain their lifestyle when they stop driving compared to those who live in less walkable areas. The results indicate that neighborhood walkability is a key element in enabling older adults to keep their independence and sustain their lifestyle.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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