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Record W4388016044 · doi:10.1093/ageing/afad125

A unified framework for the measurement of mobility in older persons

2023· article· en· W4388016044 on OpenAlexafffund
Marla Beauchamp, Qiukui Hao, Ayse Kuspinar, Jotheeswaran Amuthavalli Thiyagarajan, Christopher Mikton, Theresa Diaz, Parminder Raina

Bibliographic record

VenueAge and Ageing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsImpactMcMaster University
FundersMcMaster Institute for Research on Aging, McMaster UniversityEuropean CommissionMcMaster UniversityWorld Health Organization
KeywordsMobility modelLimited mobilityIndividual mobilityMedicineGerontologyComputer sciencePhysical medicine and rehabilitationTelecommunications

Abstract

fetched live from OpenAlex

Mobility is often referred to as a 'sixth vital sign' because of its ability to predict critical health outcomes in later adulthood. In the World Health Organization (WHO) World Report on Aging and Health, mobility is described as movement in all its forms whether powered by the body or a vehicle. As such, mobility encompasses basic physical actions such as getting up from a chair and walking, as well as activities such as exercising, driving and using public transportation. A plethora of measurement tools have been developed to assess various aspects of mobility; however, there is wide variability in the mobility constructs being measured which limits standardisation and meaningful comparison across studies. In this paper, we propose a comprehensive framework for measuring mobility that considers three distinct facets of mobility: perceived mobility ability ('what can you do'), actual mobility ability ('what you actually do') and locomotor capacity for mobility ('what could you do'). These three facets of mobility are rooted in the three components of healthy aging endorsed by the WHO: functional ability, intrinsic capacity and environments. By proposing a unified framework for measuring mobility based on theory and empirical evidence, we can advance the science of monitoring and managing mobility to ensure functional ability in older age.

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)0.001
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 score0.476
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.122
GPT teacher head0.406
Teacher spread0.284 · 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

Citations31
Published2023
Admission routes2
Has abstractyes

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