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Record W3037150074 · doi:10.1123/japa.2019-0267

Neighborhood Resources Associated With Active Travel in Older Adults—A Cohort Study in Six European Countries

2020· article· en· W3037150074 on OpenAlexaff
Erja Portegijs, Erik J. Timmermans, M.V. Castell, Elaine Dennison, Florian Herbolsheimer, Federica Limongi, Suzan van der Pas, L. Schaap, Natasja M. van Schoor, D.J.H. Deeg

Bibliographic record

VenueJournal of Aging and Physical Activity · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsSimon Fraser University
FundersMedical Research CouncilJyväskylän YliopistoDeutsche ForschungsgemeinschaftVersus ArthritisInternational Osteoporosis FoundationTampereen YliopistoEuropean CommissionBritish Heart Foundation
KeywordsPsychological interventionConfidence intervalBaseline (sea)CohortCyclingLimited resourcesOlder peopleEnvironmental healthMedicineDemographyGerontologyGeographyPsychologyPolitical scienceNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To study associations between perceived neighborhood resources and time spent by older adults in active travel. METHODS: Respondents in six European countries, aged 65-85 years, reported on the perceived presence of neighborhood resources (parks, places to sit, public transportation, and facilities) with response options "a lot," "some," and "not at all." Daily active travel time (total minutes of transport-related walking and cycling) was self-reported at the baseline (n = 2,695) and 12-18 months later (n = 2,189). RESULTS: Reporting a lot of any of the separate resources (range B's = 0.19-0.29) and some or a lot for all four resources (B = 0.22, 95% confidence interval [0.09, 0.35]) was associated with longer active travel time than reporting none or fewer resources. Associations remained over the follow-up, but the changes in travel time were similar, regardless of the neighborhood resources. DISCUSSION: Perceiving multiple neighborhood resources may support older adults' active travel. Potential interventions, for example, the provision of new resources or increasing awareness of existing resources, require further study.

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)0.001
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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