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Record W3090879156 · doi:10.1093/ageing/afaa187

Understanding consumer perceptions of frailty screening to inform knowledge translation and health service improvements

2020· article· en· W3090879156 on OpenAlexaff
Mandy M. Archibald, Michael Lawless, Rachel C. Ambagtsheer

Bibliographic record

VenueAge and Ageing · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineGerontologyThematic analysisFocus groupKnowledge translationPerceptionStakeholderQualitative researchPsychology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: despite growing support for the clinical application of frailty, including regular frailty screening for older adults, little is known about how older adults perceive frailty screening. The purpose of this study was to examine older adults' perspectives on frailty screening to inform knowledge translation and service improvements for older adults with frailty. RESEARCH DESIGN: interpretive descriptive qualitative design. PARTICIPANTS: a total of 39 non-frail (18%), pre-frail (33%) and frail or very frail (49%) South Australian older adults aged 62-99 years, sampled from community, assisted living and residential aged care settings. METHODS: seven focus groups were conducted and analysed by two independent investigators using inductive thematic analysis. RESULTS: three themes were identified. First, older adults question the necessity and logic of an objective frailty measure. Second, older adults believe any efforts at frailty screening need to culminate in an action. Third, older adults emphasise that frailty screening needs to be conducted sensitively given negative perceptions of the term frailty and the potential adverse effects of frailty labelling. DISCUSSION AND IMPLICATIONS: previous screening experiences and underlying beliefs about the nature of frailty as inevitable shaped openness to, and acceptance of, frailty screening. Findings correspond with previous research illuminating the lack of public awareness of frailty and the nascent stage of frailty screening implementation. Incorporating consumer perspectives, along with perspectives of other stakeholder groups when considering implementing frailty screening, is likely to impact uptake and optimise suitability-important considerations in person-centred care provision.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.329

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)0.0000.000

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 head0.274
GPT teacher head0.365
Teacher spread0.092 · 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

Citations41
Published2020
Admission routes1
Has abstractyes

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