0161 Greater Negative Affectivity Predicts Shorter Infant Sleep Duration
Bibliographic record
Abstract
Abstract Introduction Certain temperament characteristics in infants have been shown to be associated with infant sleep patterns. However, other sleep-related practices, such as co-sleeping and breastfeeding, are also known to be associated with sleep in infancy and are not always taken into account in studies assessing the association between temperament and sleep. Thus, this study aims to examine the associations between infant temperament and sleep parameters at six months of age, while controlling for co-sleeping and breastfeeding practices. Methods Mother-infant dyads (n=60) were recruited in the metropolitan Montreal area and consented to participate in the study. Infant sleep was reported by mothers at six months of age (±1 month) using sleep diaries (two-week period). Total nocturnal and longest consecutive sleep duration were retrieved from the diaries and averaged through the two-week period. Temperament was measured with the Infant Behaviour Questionnaire-Revised (IBQ-R). Sleep-related parental practices (co-sleeping and breastfeeding) were measured using the Sleep Practices Questionnaire (SPQ). Multiple regression analyses were conducted to determine the degree to which the temperament composite negative affectivity predicted total nocturnal and longest consecutive sleep duration while controlling for breastfeeding and co-sleeping practices. Results Regression analyses revealed significant regression models for total nocturnal (F(3,44)=6.25, p=.001, R2=.30), and longest consecutive sleep duration (F(3,44)=6.26, p=.001, R2=.30). Greater infant negative affectivity predicted shorter nocturnal sleep duration (β;;=-0.28, p=.034) and shorter longest consecutive sleep duration (β;;=-0.34, p=.010) after adjusting for breastfeeding and co-sleeping practices. Conclusion Findings suggest infants with greater negative affectivity sleep for fewer hours during the night and have shorter periods of consecutive sleep, even when sleep-related parental practices are considered. These results provide further support for the relationship between infant temperament and sleep at six months of age. Future research should investigate the relationship between infant temperament and sleep using paternal report in addition to maternal report. Support SSHRC
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".