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Record W4399705728 · doi:10.1111/1460-6984.13076

Alouette‐R normative data for French‐speaking school‐aged children living in Quebec

2024· article· en· W4399705728 on OpenAlexaffabout
Lou Champagne, Dima Safi, Bruno Gauthier

Bibliographic record

VenueInternational Journal of Language & Communication Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de Montréal
Fundersnot available
KeywordsNormativePsychologyFluencyDyslexiaPopulationReading (process)PercentileTest (biology)Developmental psychologyStatisticsMathematics educationLinguisticsDemographyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: The Alouette-R (2005) by Lefavrais is one of the most widely used tools to assess reading skills in French. However, this instrument does not have normative data specific to the French-speaking population of Quebec, Canada. AIMS: The validity of an assessment being strongly compromised when using inappropriate norms, the first objective of this study was to establish local norms for the Alouette-R. The second objective was to provide sensitivity and specificity data for each Alouette-R measure in the French-speaking Quebec population. The third objective was to compare Quebec and French normative data and their sensitivity to better understand the applicability and effectiveness of the Alouette-R test at the regional level. METHODS & PROCEDURES: A total of 347 fluent readers and 48 children with dyslexia from 3rd to 6th grades were recruited from different regions in Quebec. Participants had to read aloud the 265-word text of the Alouette-R in a maximum of 3 min. OUTCOMES & RESULTS: Norms (means, standard deviations and percentiles) by school grades were created for each test measure: reading time, number of words read, number of errors, number of words correctly read, reading accuracy index and reading fluency index. The sensitivity (i.e., the ability to correctly identify children with dyslexia) and specificity (i.e., the ability to correctly identify children without dyslexia) of these measurements were also documented. The norms and their sensitivity were then compared with those of the original French study by Lefavrais in 2005. CONCLUSIONS & IMPLICATIONS: The presence of differences between European and Quebec norms supports the importance of using local norms when assessing language skills. The reading accuracy and fluency indexes are the measurements that best discriminated children with dyslexia from those without a reading disorder in our study. This study will allow clinicians working in Quebec to have a better interpretation of the Alouette-R measurements and ultimately avoid erroneous conclusions resulting from the use of foreign normative data. WHAT THIS PAPER ADDS: What is already known on this subject The Alouette-R is a reading test validated and standardized in France to screen for dyslexia in children. The validity of existing norms with the Quebec population in Canada is questionable due to socio-linguistic differences with the population of France. What this study adds to existing knowledge This study provides for the first time normative and sensitivity/specificity data of the Alouette-R for French-speaking school-aged children living in Quebec. Differences were noted with the normative data from France, which supports the importance of using local normative data when administering reading tests in Quebec. What are the potential or actual clinical implications of this work? When administering the Alouette-R, clinicians in Quebec will now be able to use normative data adapted to the local population, which will limit erroneous conclusions resulting from the use of foreign normative data. In addition, the sensitivity and specificity values reported in the article will allow these clinicians to better interpret their results when screening for a developmental reading disorder.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.362
Teacher spread0.343 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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