Patterns of Adult-Child Linguistic Interaction in Integrated Day Care Groups
Bibliographic record
Abstract
PURPOSE: This study investigated the language input of eight childcare providers to children with developmental disabilities, including language delay, who were integrated into community day care centers. METHOD: Structural and discourse features of the adults' language input was compared across two groups (integrated, typical) and two naturalistic day care contexts (book reading, play dough activity). The eight children with developmental disabilities and language delay were between 33-50 months of age; 32 normally developing peers ranged in age from 32-53 months of age. Adult-child interactions were transcribed and coded to yield estimates of structural indices (number of utterances, rate, mean length of utterances, ratio of different words to total words used (TTR) and discourse features (directive, interactive, language-modelling) of their language input. RESULTS: The language input addressed to the children with developmental disabilities was directive and not finely tuned to their expressive language levels. In turn, these children interacted infrequently with the adult or with the other children. Contextual comparisons indicated that the play dough activity promoted adult-child interaction that was less directive and more interaction-promoting than book reading, and that children interacted more frequently in the play-dough activity. CLINICAL IMPLICATIONS: Implications for speech-language pathologists include the need for collaborative consultation in integrated settings, modification of adult-child play contexts to promote interaction, and training childcare providers to use language input that promotes communication development.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".