Approach To Families Of Children With Developmental Language Disorder From A Neurodevelopmental Perspective
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the presence of neurodevelopmental disorder symptoms in the parents of children diagnosed with language disorder (LD) and to compare these characteristics with those of parents of typically developing children. METHOD: The study included 76 children diagnosed with LD and 71 typically developing controls, along with their parents. The diagnosis of LD was based on DSM-5 criteria. Language and other developmental domains were assessed using the Denver II developmental screening test. Neurodevelopmental symptoms in parents were evaluated using the Wender-Utah Rating Scale (WURS), the Toronto Alexithymia Scale (TAS), and the Autism Spectrum Quotient (AQ). RESULTS: Maternal education level was significantly lower in the LD group compared to parents of typically developing children (p<0.001). Parents of children with LD scored significantly higher on the TAS, WURS, and AQ scales compared to the control group (all p <0.001). Deficits in speech and language abilities were observed among the children of parents who obtained high scores (p<0.001, p<0.001, p=0.006). CONCLUSION: The presence of neurodevelopmental symptoms in parents may confer the risk of language, cognitive, and other neurodevelopmental delays in their children. Early diagnosis and family-centered intervention approaches are critical to mitigating these risks and supporting psychosocial functioning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".