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Record W2064547243 · doi:10.1177/0898264314523448

Evaluation of the Late-Life Disability Instrument (LLDI) in Low-Income Older Populations

2014· article· en· W2064547243 on OpenAlexafffund
Afshin Vafaei, Fernando Gómez, Marı́a Victoria Zunzunegui, Jack M. Guralnik, Carmen‐Lucía Curcio, Ricardo Oliveira Guerra, Beatriz Alvarado

Bibliographic record

VenueJournal of Aging and Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de MontréalQueen's University
FundersCanadian Institutes of Health Research
KeywordsConstruct validityPsychologyConvergent validityCognitionConfirmatory factor analysisConstruct (python library)GerontologyExploratory factor analysisClinical psychologyScale (ratio)PsychometricsActivities of daily livingQuality of life (healthcare)Developmental psychologyMedicineStructural equation modelingPsychiatryStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the construct and convergent validity of the 16 items of the Late-Life Disability Instrument (LLDI) in Latin American seniors. METHOD: Exploratory and confirmatory factor analyses were performed to assess the factor structure of frequency and limitation sub-scales of the LLDI. ANOVA and t test were used to determine the convergent and construct validity of the LLDI by exploring associations with physical performance tests, mobility limitations, depression, cognition, self-reported health, as well as with sex, education, and income. RESULTS: Factor analysis resulted in one-factor solution and acceptable model fit. Participants with better physical function, less mobility limitations, better self-reported health, and intact cognition reported more frequent activities and fewer limitations, indicating good convergent and construct validity of our measure. CONCLUSION: LLDI is a good instrument to assess disability in low-income populations. Further research is needed to include culturally acceptable activities for the frequency sub-scale.

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)0.011
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 score0.077
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
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 head0.117
GPT teacher head0.438
Teacher spread0.322 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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