MétaCan
Menu
Back to cohort
Record W2117119516 · doi:10.1177/0898264315611668

Caregiving, Transport-Related, and Demographic Correlates of Sedentary Behavior in Older Adults

2015· article· en· W2117119516 on OpenAlexaff
Maya N. White, ­Abby C. King, James F. Sallis, Lawrence D. Frank, Brian E. Saelens, Terry L. Conway, Kelli L. Cain, Jacqueline Kerr

Bibliographic record

VenueJournal of Aging and Health · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteVanderbilt University
KeywordsSedentary behaviorGerontologySedentary lifestyleMultivariate analysisInjury preventionPhysical activityHuman factors and ergonomicsSuicide preventionPoison controlOccupational safety and healthDemographyPsychologyMedicineEnvironmental healthPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: Excess sedentary time predicts negative health outcomes independent of physical activity. The present investigation examined informal caregiving duties and transportation-related factors as potential correlates of sedentary behavior in older adults. METHOD: Average daily sedentary time was measured via accelerometer in adults ages 66 years and older (N = 861). Caregiving variables included dog ownership and informal family caregiving status. Transportation variables included driver status, walking distance to public transit, and reported presence of pedestrians and bicyclists in one's neighborhood. RESULTS: In multivariate models, owning a dog and being a driver were associated with less sedentary time (p ≤ .01). Educational status and geographic region modified the association between dog ownership and sedentary time, and age modified the association between driver status and sedentary time. DISCUSSION: This study identified that older adult dog owners and drivers were less sedentary. Both factors may create opportunities for older adults to get out of their homes.

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 score0.031
Threshold uncertainty score0.997

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 head0.026
GPT teacher head0.324
Teacher spread0.298 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Aging and HealthSame topicUrban Transport and AccessibilityFrench-language works237,207