TQ-DAV: Action Naming Test with Videos for Quebec French
Bibliographic record
Abstract
OBJECTIVE: The difficulty to retrieve verbs (verb anomia) is common in people with post-stroke aphasia. Verb anomia assessment often relies on picture oral naming task. However, oral naming of videos better captures the intrinsic dynamics of actions. METHODS: This observational study reports the development of the Test québécois de dénomination d'actions par visionnement de vidéos (TQ-DAV) [Action Naming Test with Videos for Quebec French], designed to assess verb anomia in the French-speaking individuals of Quebec, Canada. The TQ-DAV consists of 20 action videos (10 high-frequency and 10 low-frequency actions), matched on several psycholinguistic variables. RESULTS: TQ-DAV shows robust psychometric properties. It allows to differentiate the performance of healthy people and that of people with post-stroke aphasia (discriminant validity). The TQ-DAV demonstrates good internal consistency. The norms of the TQ-DAV allow to easily and automatically calculate the Z-score and the frequency effect of a person. CONCLUSION: In sum, the TQ-DAV enriches the tools for assessing verb anomia in Quebec French people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".