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Record W2985114326 · doi:10.1080/13854046.2019.1683232

Performance on the Verbal Naming Test among healthy, community-dwelling older adults

2019· article· en· W2985114326 on OpenAlexaboutno aff
Matthew J. Wynn, Annie Z. Sha, Kathleen A. Lamb, Brian Carpenter, Brian P. Yochim

Bibliographic record

VenueThe Clinical Neuropsychologist · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsBoston Naming TestNormativeCronbach's alphaPsychologyTest (biology)Construct validitySentenceCognitionDevelopmental psychologyNeuropsychologyAudiologyPsychometricsMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Objective: The Verbal Naming Test (VNT) is a nonvisual measure of word finding with stimuli chosen based on rare frequency of usage in spoken English. The purpose of the current study was to evaluate the psychometric properties of the VNT and test the feasibility of telephone administration. In addition, regression-based normative data were obtained for the VNT as well as other measures.Method: Eighty-one community-dwelling older adults 61–92 years old (mean = 74.19 years) completed the VNT, the Naming subtests of the Neuropsychological Assessment Battery (NAB), the WIAT-III Sentence Repetition subtest, and the Montreal Cognitive Assessment (MoCA).Results: As evidence of construct validity, the VNT had large correlations with the NAB Naming test and medium correlations with the MoCA and WIAT-III Sentence Repetition test. Cronbach’s alpha in this sample was 0.621. Age, education, and gender were entered into linear regression equations and regression-based normative equations are presented. Lastly, administration of the VNT over the telephone was found to be feasible.Conclusions: The VNT is a valid measure of naming among community-dwelling older adults. Regression-based normative data for the measure will enable its use in the neuropsychological assessment of naming with a wide range of older adults.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.087
GPT teacher head0.370
Teacher spread0.283 · 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.

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

Citations9
Published2019
Admission routes1
Has abstractyes

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