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Record W1984510583 · doi:10.1177/1054773810379401

Mobility Adaptations of Older Adults: A Secondary Analysis

2010· article· en· W1984510583 on OpenAlexaff
Kathy L. Rush, Wilda E. Watts, Janice Stanbury

Bibliographic record

VenueClinical Nursing Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia, Okanagan CampusOkanagan University CollegeUniversity of British Columbia
Fundersnot available
KeywordsGerontologyPsychological interventionPsychologyWeaknessPerspective (graphical)Older peopleProfiling (computer programming)MedicinePsychiatry

Abstract

fetched live from OpenAlex

The purpose of this secondary study was to describe the mobility adaptations of community-living older adults. The primary study, designed to understand weakness and aging from the perspective of older adults, revealed that older adults viewed weakness as a progression from inability to an end point of 'giving up,' which prompted the use of adaptation strategies to preserve mobility and to counter a self-identity of being weak. A qualitative descriptive design guided the primary study of 15 community-living older adults, who participated in in-depth interviews. A systematic secondary analysis using Baltes and Baltes' theory of Selective Optimization with Compensation (SOC) showed that older adults used selection, optimization, and compensation adaptations across a range of mobility behaviors. The SOC model offered a framework for profiling older adults' agency and motivations in meeting mobility challenges as they age and provided the basis for targeted interventions to maximize mobility with aging.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.144
GPT teacher head0.569
Teacher spread0.425 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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