Behaviour in Children with Language Development Disorders
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to explore the univariate and multivariate differences in behavioural problems among children with disorders in expressive or mixed receptive-expressive language development and children with unimpaired language development. METHOD: Ninety-four children with language development disorders (LDD) between the ages of 4 and 6 years and 94 children (matched by age and sex) without disorders of language development were compared concerning behavioural problems, as measured by the German version of the Child Behavior Checklist/4-18. RESULTS: Thirty-two children (34%) with LDD showed behavioural problems in the clinical range, whereas only 6 control subjects (6%) had scores in this range. Univariate group comparisons between patients and control subjects showed significant differences in all 8 syndromes and the scale "other problems," with patients having higher scores. Multivariate stepwise discriminant analysis showed a significant discriminant function by the scales "other problems," "social problems," "anxious-depressed," "thought problems," "attention problems," and "delinquent problems." CONCLUSIONS: In general, our results agree with several studies that report that children with speech and language disorders are at special risk for developing behavioural problems. Neurodevelopmental immaturity may be one factor underlying both the disorder in language development and the behavioural problems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".