Associations between early maternal sensitivity and children's sleep throughout early childhood
Bibliographic record
Abstract
Abstract Despite strong theoretical reasons to believe that the quality of parent–infant interactions should influence child sleep, the empirical evidence for links between maternal behavior and children's sleep is equivocal. Notably, it is unclear at which ages such influences might be particularly salient. The current study aimed to examine prospective longitudinal associations between early maternal sensitivity and children's sleep during early childhood. Maternal sensitivity was assessed at 12 months during a home visit. Children's sleep was measured at 12 and 18 months as well as at 2, 3, and 4 years, using a sleep diary completed by mothers. Results revealed significant or marginal positive associations between maternal sensitivity and children's sleep consolidation (percentage of nighttime sleep) at 2, 3, and 4 years, but not at the most proximal assessments of 12 and 18 months. These findings suggest that child age could potentially be a key factor in the associations between maternal behavior and children's sleep. Highlights Associations between three dimensions of early maternal sensitivity and children's sleep from 1 to 4 years of age were investigated. Maternal sensitivity was positively associated with children's sleep between 2 and 4 years, but not at 12 and 18 months. The results suggest that child age could be a key factor in the associations between maternal behavior and children's sleep.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".