Associations between maternal responsive linguistic input and child language performance at age 4 in a community‐based sample of slow‐to‐talk toddlers
Bibliographic record
Abstract
BACKGROUND: In a community sample of slow-to-talk toddlers, we aimed to (a) quantify how well maternal responsive behaviors at age 2 years predict language ability at age 4 and (b) examine whether maternal responsive behaviors more accurately predict low language status at age 4 than does expressive vocabulary measured at age 2 years. DESIGN OR METHODS: Prospective community-based longitudinal study. At child age 18 months, 1,138 parents completed a 100-word expressive vocabulary checklist within a population survey; 251 (22.1%) children scored ≤20th percentile and were eligible for the current study. Potential predictors at 2 years were (a) responsive language behaviors derived from videotaped parent-child free-play samples and (b) late-talker status. Outcomes were (a) Clinical Evaluation of Language Fundamentals-Preschool Second Edition receptive and expressive language standard score at 4 years and (b) low language status (standard score > 1.25 standard deviations below the mean on expressive or receptive language). RESULTS: = 3.5%) language scores at 4. The logistic regression model containing only responsive behaviors achieved "fair" predictive ability of low language status at age 4 (area under curve [AUC] = 0.79), slightly better than the model containing only late-talker status (AUC = 0.74). This improved to "good" predictive ability with inclusion of other known risk factors (AUC = 0.82). CONCLUSION: A combination of short measures of different dimensions, such as parent responsive behaviors, in addition to a child's earlier language skills increases the ability to predict language outcomes at age 4 to a precision that is approaching clinical value. Research to further enhance predictive values should be a priority, enabling health professionals to identify which slow-to-talk toddlers most likely will or will not experience later poorer language.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".