The Interface between ADHD and Language Impairment: An Examination of Language, Achievement, and Cognitive Processing
Bibliographic record
Abstract
Language impairments are commonly observed among children referred for psychiatric services. The most frequent psychiatric diagnosis of children with language impairment (LI) is Attention Deficit Hyperactivity Disorder (ADHD). It is not clear whether there are differences between children with ADHD and comorbid LI and children with other psychiatric disorders who are also comorbid for LI. In the present study the language, achievement, and cognitive processing characteristics of 166 psychiatrically referred 7-14-year-old children were examined using a 2 x 2 (ADHD, LI) design to examine four groups: children with ADHD + LI, children with ADHD who have normally developing language, children with psychiatric diagnoses other than ADHD with a language impairment (OPD + LI) or without a LI (OPD). Results indicated that children with LI were at the most disadvantage regardless of the nature of the psychiatric diagnosis. Contrary to prediction, working memory measures, used to tap the core cognitive deficit of ADHD in executive functions, were more closely associated with LI than with ADHD. It was concluded that caution must be exercised in attributing to children with ADHD what might be a reflection of problems for children with language impairment generally. As most therapies are verbally based it is notable that language competence is rarely evaluated systematically before such therapies are undertaken.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".