Associations between Maternal Behaviors at 1 Year and Child Language at 2 Years in a Cohort of Women Experiencing Adversity
Bibliographic record
Abstract
Variations in parenting have been suggested as contributing to a higher prevalence of language difficulties in children experiencing economic, environmental, and social adversity. Within these cohorts, the contribution of responsive and intrusive parenting to child language has been investigated; specific responsive and intrusive behaviors encapsulated within these parenting styles have yet to be fully examined. Additionally, the role of the mother–child dynamic in moderating mother–child associations has also not been explored. This study aimed to augment current research by identifying specific responsive and intrusive maternal behaviors associated with child language in a cohort experiencing adversity, as well as exploring the role of the fluency and connectedness of mother–child conversation in moderating associations (n = 249). Specific behaviors and the fluency and connectedness of the mother–child interaction were coded from free‐play videos at child age 12 months. Child language measures were derived from transcripts of free play at 24 months. Linear regression models were used to examine maternal–child associations. The moderating role of fluency and connectedness was then explored. Maternal imitations were positively associated with the child's total words; successful redirectives were negatively associated with the child's mean length of turn. Both associations were moderated by the fluency and connectedness of the interaction.
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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 | 0.002 |
| 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 | 0.000 |
| 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".