Associations between maternal responsive linguistic input and child language performance at age 4 in a community‐based sample of slow‐to‐talk toddlers
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it