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Record W3116297093 · doi:10.1177/0733464820979258

Perceptions of the Neighborhood Built Environment for Walking Behavior in Older Adults Living in Close Proximity

2020· article· en· W3116297093 on OpenAlexafffund
Florian Herbolsheimer, Atiya Mahmood, Nadine Ungar, Yvonne L. Michael, Frank Oswald, Habib Chaudhury

Bibliographic record

VenueJournal of Applied Gerontology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsSimon Fraser University
FundersInstitute of AgingNational Institute on AgingCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsPerceptionBuilt environmentPsychologyGerontologyAging in placeAssisted Living FacilityMedicineAssisted livingEngineering

Abstract

fetched live from OpenAlex

Past research documents a discordance between perceived and objectively assessed neighborhood environmental features on walking behavior. Therefore, we examined differences in the perception of the same neighborhood built environment. Participants were grouped if they lived 400 m or closer to each other. The perception of the pedestrian infrastructure, neighborhood aesthetics, safety from crime, and safety from traffic was derived from a telephone survey from two North American metropolitan areas; 173 individuals were clustered into 42 groups. Older adults who walked for transport in their neighborhood experienced the same neighborhood as more walkable (β = .19; p = .011) with better pedestrian infrastructure (β = .16; p = .037). Older adults with physical limitations experienced the same neighborhood as less safe from crime (β = −.17; p = .030) and traffic (β = −.20; p = .009). The study supports the notion that individual behavior and physical restrictions alter the environment’s perception and explains part of the discordance between objective and subjective assessment of the neighborhood environment.

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 score0.051
Threshold uncertainty score0.420

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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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