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Record W2899968769 · doi:10.1093/geroni/igy023.1884

THE ASSOCIATION BETWEEN LIFE SPACE MOBILITY AND COGNITION IN OLDER ADULTS

2018· article· en· W2899968769 on OpenAlexaff
N De Silva, Shree Venkateshan, Ayse Kuspinar

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCognitionCINAHLAssociation (psychology)PsycINFOPsychologyGerontologyMedicineMEDLINEPsychiatry

Abstract

fetched live from OpenAlex

As people age, both mobility and cognitive function may decline at varying rates. This systematic review evaluated the relationship between cognition and life space mobility in older adults. Electronic databases PsycINFO, Embase, Ovid and CINAHL were searched using key terms including ‘life space’ AND ‘cognition’, yielding 163 abstracts for screening. Data were extracted from 31 peer-reviewed articles in English that included subjects over the age of 60, as well as quantitative measures describing the association between life space mobility and cognition. The range of correlation coefficients between life space mobility and the Mini-Mental State Examination were r= -0.47 to .491. Correlation values were r = -.128 to .259 for life space mobility and executive function, r= .206 to .230 for memory, and r= -.185 to .365 for perceptual and processing speed. Executive function had the highest predictive capacity for life-space mobility. Life space scores <40 were associated with significantly more long-term cognitive impairment as compared to those who were unrestricted. R-squared values for cognition and life-space mobility ranged from .113 to .491 explaining a small to moderate amount of variance. Furthermore, findings demonstrated that associations between cognition and life space may be mediated through sociodemographic, physical performance and mental status.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.011
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.040
GPT teacher head0.390
Teacher spread0.350 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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