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Record W2017125467 · doi:10.1177/0898264309333313

The Use of Mobility Devices Among Institutionalized Older Adults

2009· article· en· W2017125467 on OpenAlexaffabout
Philippa Clarke, Pamela Chan, Pasqualina Santaguida, Angela Colantonio

Bibliographic record

VenueJournal of Aging and Health · 2009
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsToronto Rehabilitation InstituteMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsWheelchairSocioeconomic statusOddsGerontologySocial mobilityMultinomial logistic regressionSample (material)PsychologyMedicineLogistic regressionEnvironmental healthPopulationComputer scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this article is to examine the demographic, health, and social characteristics of mobility device users in long-term care settings. METHODS: Data were used from a recently institutionalized sample of older adults from the Canadian Study of Health and Aging. Multinomial logistic regression was used to examine the factors associated with the use of different mobility devices (cane, walker, or wheelchair). RESULTS: Over 70% used mobility aids (over 50% used a wheelchair). Mobility limitations were strongly associated with the use of mobility devices. However, among those with mobility limitations, educational resources reduced the odds of wheelchair use. CONCLUSIONS: Consistent with findings from the community setting, need factors are strongly associated with the use of mobility aids in institutions. However, socioeconomic resources may provide older adults with alternate ways to manage mobility limitations in institutional settings.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.141
GPT teacher head0.467
Teacher spread0.326 · 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 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

Citations37
Published2009
Admission routes2
Has abstractyes

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