Intervention for improving comprehension in 4–6 year old children with specific language impairment: practicing inferencing is a good thing
Bibliographic record
Abstract
Few studies report on therapy to improve language comprehension in children with specific language impairment (SLI). We address this gap by measuring the effect of a systematic intervention to improve inferential comprehension using dialogic reading tasks in conjunction with pre-determined questions and cues. Sixteen children with a diagnosis of SLI aged 4-6 participated in 10 weekly treatment sessions carried out by their regular therapists. Baseline and maintenance periods were also tabulated. Two experimental measures and a standardized test revealed that children's total scores and the quality of their responses post-treatment were better than those obtained pre-treatment. However, perhaps due to the use of non-equivalent probes, this change could not be interpreted solely as a significant effect of intervention. These results nevertheless suggest that a systematically designed intervention focusing on the comprehension of specific types of questions requiring inferencing and using a carefully scaffolded cueing strategy can be beneficial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".