Longitudinal perspective on nonverbal intelligence development in young children with developmental language disorder
Notice bibliographique
Résumé
BACKGROUND: Nonverbal intelligence has been linked to language impairments and adaptational outcomes in clinical populations. However, the development of nonverbal intelligence in conjunction with language difficulties is still poorly understood. AIMS: This study aims to characterize the progression of nonverbal intelligence in young children with Developmental Language Disorder (DLD). METHODS AND PROCEDURES: This study collected data from medical records of children seen in a child psychiatric clinic. The sample consisted of 71 children diagnosed with DLD who had completed two Wechsler scale assessments. The first assessment took place at the mean age of 4:11 years, and the second at the mean age of 8:2 years. OUTCOMES AND RESULTS: Three groups were formed according to the evolution of nonverbal intelligence: decrease (n = 22), increase (n = 21), and stability (n = 28). Multivariate analyses of covariance indicated that initial verbal and nonverbal intellectual skills, multilingualism, and age distinguished these three groups and effects were medium to large. Children in the increasing path are significantly younger and have significantly lower initial verbal and nonverbal intellectual skills. CONCLUSIONS AND IMPLICATIONS: Evolution of nonverbal development in children with DLD seems highly variable. More studies are needed, but very young children with DLD may not be able to demonstrate their full intellectual potential in standardized Weschler assessments. It would be advisable to continue to follow the evolution of their abilities with caution to personalize interventions. WHAT THIS PAPER ADDS: Developmental Language Disorder (DLD) is a diagnosis directly related to expressive and receptive language difficulties. Therefore, assessment and intervention are focused on verbal and language ability. Children with DLD also seem to have nonverbal cognitive weaknesses, but the understanding of nonverbal development in this population is limited. Among school-aged children, a great deal of variation in nonverbal abilities according to age is observed, possibly linked to the type of assessment used. Nonverbal intelligence has been related to functional outcomes in children, adolescents, and adults with DLD, thus warranting further investigation. This paper explores different nonverbal intelligence developmental evolutions of children from diverse ethnic groups, ensuring representation from large urban areas, and the clinical factors related to those trajectories. Three developmental profiles were differentiated: increase (29.6 %), stability (39.4 %), and decrease (31 %), which were distinguished by initial verbal and nonverbal intelligence as well as age. Having weaker verbal and nonverbal intelligence and being younger were associated with increasing nonverbal intelligence between the two time points. Development of nonverbal intelligence seems highly variable among preschoolers diagnosed with DLD who consulted in a clinical setting, with children being just as likely to improve, maintain, or decrease in ability. These findings may also indicate that nonverbal intelligence assessments may not capture the true nonverbal potential of younger children with DLD, especially when they show an array of difficulties. More research is needed to understand the different trajectories of nonverbal development, but current results encourage caution in the intellectual assessments of children with DLD.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».