Longitudinal perspective on nonverbal intelligence development in young children with developmental language disorder
Bibliographic record
Abstract
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.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".