Cognitive and linguistic effects of narrative-based language intervention in children with Developmental Language Disorder
Bibliographic record
Abstract
Background and aims: Narrative-based language intervention provides a naturalistic context for targeting overall story structure and specific syntactic goals in children with Developmental Language Disorder (DLD). Given the cognitive demands of narratives, narrative-based language intervention also has the potential to positively impact related abilities such as working memory and academic skills. Methods: Ten children (8-11 years old) with DLD completed 15 sessions of narrative-based language intervention. Results: Results of single subject data revealed gains in language for five participants, four of whom improved on a probe tapping working memory. An additional four participants improved on a working memory probe only. On standardized measures, clinically significant gains were noted for one additional participant on a language measure and one additional participant on a visuospatial working memory. Carry over to reading was noted for three participants and to math for one participant. Across measures, gains in both verbal and visuospatial working memory were common. A responder analysis revealed that improvement in language may be associated with higher verbal short-term memory and receptive language at baseline. Those with working memory impairments were among those showing the fewest improvements across measures. Conclusions: Narrative-based language intervention impacted verbal skills in different ways across individual children with DLD.Implications: Further research is needed to gain an understanding of who benefits most from narrative-based language intervention.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".