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Record W2340144526

"It’s not all about me": interactive effects of psychosocial and built-environment variables on physical-activity

2015· article· en· W2340144526 on OpenAlexaboutno aff
Lena Fleig, Christine Voss, Maureen C. Ashe, Suzanne Therrien, Joanie Sims‐Gould, Heather McKay, Meghan Winters

Bibliographic record

VenueEuropean Health Psychologist · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)PsychosocialPhysical activityFeelingPsychologyAssociation (psychology)GerontologyMedicineSocial psychologyPhysical therapyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Background: Neighbourhood environments can support or hinder physical activity. This study examines how individual-level factors interact with environment-factors associated with physical activity in older adults. Methods: The Active Streets, Active People study recruited 193 older adults living in a highly walkable neighbourhood in Vancouver. Participants completed questionnaires on walking attitudes, gait efficacy, social support, and neighbourhood satisfaction. To assess physical activity, participants wore an accelerometer. To test whether neighbourhood satisfaction moderated the association of individual-level variables with physical activity we estimated multiple linear regression models with interaction terms. Findings: In total, 173 had valid accelerometry data and demonstrated high levels of daily moderate-to-vigorous physical activity (M=39.7,SD=34.1 minutes/day). Individual-level factors associated with MVPA were age and attitudes towards walking. Neighbourhood satisfaction moderated the association between gait efficacy and MVPA with stronger associations between gait efficacy and MVPA in individuals with higher levels of neighbourhood satisfaction. Discussion: Our findings suggest that feeling confident about walking ability is not sufficient to encourage physical activity. Only when individuals are satisfied with their neighbourhood will this translate into behavior.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.093
GPT teacher head0.412
Teacher spread0.319 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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