Parenting Behavior and Child Language: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Early language development supports cognitive, academic, and behavioral success. Identifying modifiable predictors of child language may inform policies and practices aiming to promote language development. OBJECTIVE: To synthesize results of observational studies examining parenting behavior and early childhood language in typically developing samples. DATA SOURCES: Searches were conducted in Medline, Embase, PsycINFO, Web of Science, and Dissertation Abstracts (1967 to 2017). STUDY SELECTION: Studies had 1 of 2 observational measures of parenting behavior (i.e., sensitive responsiveness or warmth) and a measure of child language. DATA EXTRACTION: Data from 37 studies were extracted by independent coders. Estimates were examined by using random-effects meta-analysis. RESULTS: = 0.16; 95% confidence interval: 0.09 to 21). The pooled effect size for the association between sensitive responsiveness and child language was statistically higher than that of warmth and child language. The association between sensitive responsiveness and child language was moderated by family socioeconomic status (SES): effect sizes were stronger in low and diverse SES groups compared with middle to upper SES groups. Effect sizes were also stronger in longitudinal versus cross-sectional studies. LIMITATIONS: Results are limited to typically developing samples and mother-child dyads. Findings cannot speak to causal processes. CONCLUSIONS: Findings support theories describing how sensitive parenting may facilitate language and learning.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".