T-DAV : Test de dénomination d’actions par visionnement de vidéos. Développement, validation et normalisation
Bibliographic record
Abstract
Word finding difficulties, particularly for verbs, are a common symptom in post-stroke aphasia and people with neurodegenerative diseases, such as Alzheimer’s disease. Word finding difficulties for verbs are mainly assessed by action naming tasks, using often images depicting actions. However, videos seem to be more adapted than images for action naming. To date, there are no action naming tests using videos available in French. The aim of this study is to present the T-DAV, an action naming test with videos, and its psychometric properties (validity and reliability). The T-DAV is composed of 20 videos (10 high frequency and 10 low frequency actions). High and low frequency stimuli are matched for several relevant psycholinguistic variables (e.g., length in phonemes). Performance on the T-DAV is associated with performance on the DVL-38 test, a French action naming test using images (concurrent validity). The T-DAV allows to differentiate the performance of healthy individuals from that of Alzheimer’s Disease patients (discriminant validity). The items of the T-DAV show good internal consistency (reliability). In sum, the T-DAV shows good pshychometric properties and counts with norms for French-speaking adults. The T-DAV fulfills a clinical need of action naming tests with videos in French.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".