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Older adults’ perceptions of weakness and ageing

2011· article· en· W1919703445 on OpenAlexafffund
Kathy L. Rush, Wilda E. Watts, Janice Stanbury

Bibliographic record

VenueInternational Journal of Older People Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsWeaknessPerceptionGerontologyMuscle weaknessSarcopeniaPsychologyAssociation (psychology)Medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Age-related weakness, or sarcopenia, has been related to functional disability, falls, frailty and mortality. Although it is one of the most common symptoms older adults link to their functional abilities, to date, no studies have explored older adults' perceptions of weakness and its association with ageing. AIMS AND OBJECTIVES: To understand the meaning of weakness for older adults' and their perceptions of its association with ageing. DESIGNS: A qualitative descriptive design involved in-depth interviews with 13 community-dwelling older adults. RESULTS: Weakness was described primarily in two ways: (i) inability and (ii) inward turning. The extreme of weakness, as giving up and giving in, which older adults applied to others, not themselves, prompted them to engage in several efforts at staying strong. These included motivating self-talk, achieving balance, keeping busy and active, and self-validating as a strong person. Older adults' perceptions of the association between weakness and ageing were variable and characterized by considerable ambiguity. IMPLICATIONS FOR PRACTICE: Nurses must be alert to both the visible and subtler dimensions of weakness. It is important to engage older adults in active strategies that enhance muscle strength while capitalizing on their self-motivating and validating efforts at staying strong.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.634
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.017
GPT teacher head0.298
Teacher spread0.282 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

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