Performance on the Verbal Naming Test among healthy, community-dwelling older adults
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".