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Record W3115197255 · doi:10.1093/geroni/igaa057.2335

An International Comparison of the Community Mobility Patterns of Older Adults

2020· article· en· W3115197255 on OpenAlexaff
Lizette Swanepoel, Isabelle Gélinas, Barbara Mazer

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcGill University
Fundersnot available
KeywordsResidenceGerontologyQuality of life (healthcare)Older peopleCohortPublic transportPsychologyMedicineDemographySociologyTransport engineeringNursingEngineering

Abstract

fetched live from OpenAlex

Abstract Community mobility in older adults is important for maintaining health, quality of life and social participation. Globally, older adults who are non-drivers, access their community through various modes of transport to maintain community mobility. This international cross-sectional cohort study (n=246) explored the mobility patterns of older adults and examined their access to out-of-home activities and health related quality of life in seven countries. Quality of life was determined using EQ-5D-5L and was generally high among all participants. Findings from the study indicate that a complex myriad of factors influence safe transport mobility in older adults. Results suggested that inclement weather and place of residence negatively impacted access to out-of-home activities, yet these factors but did not increase use of public transport. Given the complexity of transportation use and mobility patterns in older adults, an individualised approach may be necessary to keep older adults connected to their out-of-home activities.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.371
Teacher spread0.313 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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