Non‐linguistic cognitive measures as predictors of functionally defined developmental language disorder in monolingual and bilingual children
Notice bibliographique
Résumé
BACKGROUND & AIMS: Given that standardized language measures alone are inadequate for identifying functionally defined developmental language disorder (fDLD), this study investigated whether non-linguistic cognitive abilities (procedural learning, motor functions, executive attention, processing speed) can increase the prediction accuracy of fDLD in children in linguistically diverse settings. METHODS & PROCEDURES: We examined non-linguistic cognitive abilities in mono- and bilingual school-aged children (ages 8-12) with and without fDLD. Typically developing (TD) children (14 monolinguals, 12 bilinguals) and children with fDLD (28 monolinguals, 12 bilinguals) completed tasks measuring motor functions, procedural learning, executive attention and processing speed. Children were assigned as fDLD based on parental or professional concerns regarding children's daily language functioning. If no concerns were present, children were assigned as TD. Standardized English scores, non-verbal IQ scores and years of maternal education were also obtained. Likelihood ratios were used to examine how well each measure separated the fDLD versus TD groups. A binary logistic regression was used to test whether combined measures enhanced the prediction of identifying fDLD status. OUTCOMES & RESULTS: A combination of linguistic and non-linguistic measures provided the best distinction between fDLD and TD for both mono- and bilingual groups. For monolingual children, the combined measures include English language scores, functional motor abilities and processing speed, whereas for bilinguals, the combined measures include English language scores and procedural learning. CONCLUSIONS & IMPLICATIONS: A combination of non-linguistic and linguistic measures significantly improved the distinction between fDLD and TD for both mono- and bilingual groups. This study supports the possibility of using non-linguistic cognitive measures to identify fDLD in linguistically diverse settings. WHAT THIS PAPER ADDS: What is already known on the subject Given that standardized English language measures may fail to identify functional language disorder, we examined whether supplementing English language measures with non-linguistic cognitive tasks could resolve the problem. Our study is based on the hypothesis that non-linguistic cognitive abilities contribute to language processing and learning. This is further supported by previous findings that children with language disorder exhibit non-linguistic cognitive deficits. What this paper adds to existing knowledge The results indicated that a combination of linguistic and non-linguistic cognitive abilities increased the prediction of functional language disorder in both mono- and bilingual children. What are the potential or actual clinical implications of this work? This study supports the possibility of using non-linguistic cognitive measures to identify the risk of language disorder in linguistically diverse settings.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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