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Record W3011965826 · doi:10.1111/nhs.12713

The Reported Edmonton Frail <scp>Scale‐Thai</scp> version: Development and Validation of a <scp>Culturally‐Sensitive</scp> Instrument

2020· article· en· W3011965826 on OpenAlexaboutno aff
Inthira Roopsawang, Hilaire J. Thompson, Oleg Zaslavsky, Basia Belza

Bibliographic record

VenueNursing and Health Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Reliability (semiconductor)Confirmatory factor analysisGerontologyMedicineVulnerability (computing)Content validityClinical psychologyPsychometricsPsychologyStructural equation modelingComputer science

Abstract

fetched live from OpenAlex

Frailty may lead to increased vulnerability, disability, and adverse health outcomes in older adults. Early detection has been described as the best approach to manage frailty; however, frailty instruments are not widely available, particularly in the Thai language. The purpose of this cross-sectional study was to develop a culturally adapted Thai version of the Reported Edmonton Frail Scale and to validate the psychometric properties of the new instrument in hospitalized older Thai adults. Reliability and validity were examined. Participants completed questionnaires that included demographic and health information, and the Reported Edmonton Frail Scale-Thai version. Results revealed that the new instrument was reliable and had good content validity. Inter-rater reliability was strong. Confirmatory factor analysis showed a fair fit for the whole model, but most domains were strongly associated with frailty. On average, the instrument was completed under 7 minutes. The Thai version of the frailty instrument may be a practical tool for frailty evaluation, and could inform inpatient care, both locally and internationally; future research is needed to confirm predictability and feasibility in other clinical settings and populations.

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)0.000
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 score0.622
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.065
GPT teacher head0.329
Teacher spread0.264 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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