THE VALIDITY AND RELIABILITY OF THE VERBAL NAMING TEST
Bibliographic record
Abstract
Word finding difficulty (i.e., anomia) is a symptom of several neurological disorders. Well validated instruments exist to assess anomia but are limited in their utility because of their format. The current study provides psychometric data on a new word-finding test, the Verbal Naming Test (VNT), which can be administered orally, therefore making it useful for people with vision impairments and color blindness, and allowing it to be administered by telephone. Seventy-four healthy older adult participants (ages 65–92) were recruited to complete the 52-item VNT, the naming test from the Neuropsychological Assessment Battery (NAB), the Montreal Cognitive Assessment (MoCA), and the Sentence Repetition subtest of The Wechsler Individual Achievement Test – Third Edition (WIAT-III). A subsample completed a retest of the VNT (mean interval = 4.07 days). Test-retest reliability was good (r = 0.761, p < 0.001). Correlations between the VNT and the NAB were positive and strong (r = 0.730, p < 0.001), suggesting good convergent validity. Good divergent validity was suggested by moderate correlations between the VNT and the MoCA (r = 0.269, p < 0.029) and the VNT and the Sentence Repetition task of WIAT-III (r = 0.383, p < 0.001). Total scores on the VNT were related to age (r = -0.369, p < 0.001) and education (r = 0.499, p < 0.001) and unrelated to gender and self-reported income. This study found the VNT to be an accessible, easy-to-administer measure of naming ability useful in both clinical and research settings.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".