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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 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 score0.096
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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