Responsiveness of Child Care Providers in Interactions With Toddlers and Preschoolers
Bibliographic record
Abstract
PURPOSE: This exploratory study investigated the responsive language input of 26 child care providers to young children enrolled in community child care centers. METHOD: Three subtypes of responsive interaction strategies were rated and compared across two age groups (toddlers, preschoolers) and two naturalistic contexts (book reading, play dough activity). The toddlers were between 17 and 33 months of age and the preschoolers were between 30 and 53 months of age. Caregiver-child interactions were rated using the Teacher Interaction and Language Rating Scale (Girolametto, Weitzman, & Greenberg, 2000) to provide information about the frequency of responsive language strategies. RESULTS: Caregivers used similar levels of child-centered and interaction-promoting strategies with both age groups, but used more labelling with toddlers and more topic extensions with preschoolers. The context of the interaction exerted a systematic influence on the caregivers' use of responsive strategies, with the play dough activity providing the most responsive input overall. There was a strong positive relationship between all three subtypes of caregivers' responsiveness and variation in the preschoolers' language productivity. In contrast, only interaction-promoting strategies were positively related to measures of the toddlers' language productivity. CLINICAL IMPLICATIONS: The results of this study suggest that caregivers' responsiveness in group interactions is highly dependent on the context of the interaction and, to a lesser extent, on the language abilities of the children. Future research is required to determine if inservice training can enhance levels of responsiveness and accelerate language learning in young children in group care.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".