Maternal Behaviors Promoting Language Acquisition in Slow-to-Talk Toddlers
Bibliographic record
Abstract
OBJECTIVE: To determine, in a community-based sample of slow-to-talk toddlers, the extent to which specific maternal responsive behaviors at 24 months predict child language at 24 and 36 months. METHODS: Mother-child dyads were recruited for this prospective longitudinal study from 3 local government areas spanning low, middle, and high socioeconomic status in Melbourne, Australia. At child age 18 months, 1138 parents completed a 100-word expressive vocabulary checklist; the 251 (22.1%) children scoring ≤20th percentile were then followed up to comprise this study. PREDICTORS: Six maternal responsive behaviors (imitations, interpretations, labels, expansions, supportive directives and responsive questions) were derived from mother-child free-play videos collected at 24 months of age and coded using the Observer XT system. OUTCOMES: Expressive and receptive language measured at 24 and 36 months of age (Preschool Language Scale-4), blind to maternal responsiveness ratings. RESULTS: Two hundred and twenty-six of the 251 (90.0%) mother-child dyads were followed up at 36 months. In confounder-adjusted linear regression analyses, expansions, imitations, and responsive questions were strongly associated with better receptive and expressive language at 24 and 36 months. Labels unexpectedly predicted poorer expressive language at 36 months. Expansions were the only maternal behavior that predicted improvement in language between 24 and 36 months. CONCLUSIONS: Maternal responsive behaviors, particularly expansions, offer promise in enhancing early language learning in slow-to-talk toddlers. Parent-child interactions characterized by frequent use of maternal labels at 24 months could also be a predictive marker of those slow-to-talk toddlers at greater risk of persistent language problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".