Developmental Language Disorder at Adolescence: Links Between Communication Skills and Self-Efficacy Ratings
Bibliographic record
Abstract
Developmental Language Disorder (DLD) has a prevalence of 7%, making it one of the important yet little known neurodevelopmental disorders. Often identified in kindergarten, children with DLD have language learning difficulties severe enough to impact their schooling and socialization. During adolescence, there is a high risk of academic, vocational, and mental health difficulties. However, for adults with DLD, a positive perception of self-efficacy may act as a protective factor. This led us to explore how communications skills and self-efficacy are related in adolescents with DLD. The participants were 49 teenagers, aged 12 to 15, who have DLD. Communication was measured using the Children’s Communication Checklist, completed by the teenagers’ parents, while perception of self-efficacy was reported by the teenagers as well as by their parents using the Generalized Self-Efficacy Scale. A significant correlation was found between communication and self-efficacy with three domains of communication being the most important in this association, i.e., coherence (r = 0.716, p < 0.001), initiation (r = 0.581, p < 0.001), and use of context (r = 0.649, p < 0.001). These results highlight the crucial role of social communication in the profile of teenagers living with DLD. They further suggest that it may be relevant to examine whether supporting language development may foster positive perception of self-efficacy in teenagers living 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".