MétaCan
Menu
Back to cohort
Record W2790165137 · doi:10.1080/13854046.2018.1429670

Normative data for the Rey Auditory Verbal Learning Test in the older French-Quebec population

2018· article· en· W2790165137 on OpenAlex
Monica Lavoie, Louis Bherer, Sven Joubert, Jean‐François Gagnon, Sophie Blanchet, Isabelle Rouleau, Joël Macoir, Carol Hudon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Clinical Neuropsychologist · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité LavalUniversité de MontréalUniversité du Québec à MontréalInstitut Universitaire de Gériatrie de MontréalMontreal Heart Institute
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsW. Garfield Weston Foundation
KeywordsNormativeRecallTest (biology)PsychologyPopulationCalifornia Verbal Learning TestDevelopmental psychologyRegression analysisVerbal learningCognitionDemographyCognitive psychologyStatisticsPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.219
GPT teacher head0.453
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it