Normative data for the Rey Auditory Verbal Learning Test in the older French-Quebec population
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to establish normative data for the Rey Auditory Verbal Learning Test, a test assessing verbal episodic memory, in the older French-Quebec population. METHOD: A total of 432 French-speaking participants aged between 55 and 93 years old, from the Province of Quebec (Canada), were included in the study. Using multiple regression analyses, normative data were developed for five variable of interest, namely scores on trial 1, sum of trials 1 to 5, interference list B, immediate recall of list A, and delayed recall of list A. RESULTS: Results showed that age, education, and sex were associated with performance on all variables. Equations to calculate the expected score for a participant based on sex, age, and education level as well as the Z score were developed. CONCLUSION: This study provides clinicians with normative data that take into account the participants' sociodemographic characteristics, thus giving a more accurate interpretation of the results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".