Association between maternal sensitivity and child receptive language development: Quasi-causal evidence using a sibling comparison design.
Bibliographic record
Abstract
Observational studies have shown that caregiver sensitivity predicts child language skills. These studies, however, have entirely relied on between-family designs (single parent-child dyad per family), which cannot rule out the contribution of shared family confounds (e.g., genetics, books in home). The current study investigates whether observed caregiver sensitivity predicts changes in child receptive language using a sibling comparison design. Participants were 890 Canadian children (51.7% male; 52.4% White) nested within 447 families from diverse socioeconomic backgrounds with children between the ages of 2 and 3.5 years (Wave 1) and 3.5 and 5 years (Wave 2). Independent observers provided ratings of maternal sensitivity with each sibling using several coding protocols (i.e., Coding of Attachment-Related Parenting and the Parent-Child Interaction System). Child receptive language was assessed using the Peabody Picture Vocabulary Test. Maternal sensitivity predicted within-person change in receptive language. That is, the sibling that receives comparatively more sensitivity from the caregiver showed more development in language over time when compared to their sibling. The obverse association, child language to later maternal sensitivity, was not observed, pointing to a unidirectional association of maternal sensitivity on child receptive language. Our sibling comparison design rules out the role of shared family confounders, which provides a strong test of causal processes within an observational design. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".