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Record W2105048386 · doi:10.1093/geront/gnq013

Mobility in Older Adults: A Comprehensive Framework

2010· article· en· W2105048386 on OpenAlexafffund
Sandra C. Webber, Michelle M. Porter, Verena Menec

Bibliographic record

VenueThe Gerontologist · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of ManitobaResearch Manitoba
FundersCanadian Institutes of Health Research
KeywordsPsychosocialBridging (networking)MobilitiesPsychologyCognitionSociologyComputer scienceSocial scienceComputer security

Abstract

fetched live from OpenAlex

Mobility is fundamental to active aging and is intimately linked to health status and quality of life. Although there is widespread acceptance regarding the importance of mobility in older adults, there have been few attempts to comprehensively portray mobility, and research has to a large extent been discipline specific. In this article, a new theoretical framework for mobility is presented with the goals of raising awareness of the complexity of factors that influence mobility and stimulating new integrative and interdisciplinary research ideas. Mobility is broadly defined as the ability to move oneself (e.g., by walking, by using assistive devices, or by using transportation) within community environments that expand from one's home, to the neighborhood, and to regions beyond. The concept of mobility is portrayed through 5 fundamental categories of determinants (cognitive, psychosocial, physical, environmental, and financial), with gender, culture, and biography (personal life history) conceptualized as critical cross-cutting influences. Each category of determinants consists of an increasing number of factors, demonstrating greater complexity, as the mobility environment expands farther from the home. The framework illustrates how mobility impairments can lead to limitations in accessing different life-spaces and stresses the associations among determinants that influence mobility. By bridging disciplines and representing mobility in an inclusive manner, the model suggests that research needs to be more interdisciplinary and current mobility findings should be interpreted more comprehensively, and new more complex strategies should be developed to address mobility concerns.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0040.007
Scholarly communication0.0050.008
Open science0.0020.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.412
Teacher spread0.362 · 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 designTheoretical or conceptual
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

Citations910
Published2010
Admission routes2
Has abstractyes

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