Mid‐pregnancy and postpartum maternal mental health and infant sleep in the first year of life
Bibliographic record
Abstract
Perinatal depression and anxiety are common and associated with sleep problems in the offspring. Depression and anxiety are commonly comorbid, yet often studied independently. Our study used an integrative measure of anxiety and depressive symptoms to examine the associations of maternal mental health (mid-pregnancy and postnatal) with infant sleep during the first year of life. A total of 797 mother-child dyads from the 'Growing Up in Singapore Towards healthy Outcome' cohort study provided infant sleep data at 3, 6, 9 and 12 months of age, using the caregiver reported Brief Infant Sleep Questionnaire. Maternal mental health was assessed at 26-28 weeks gestation and 3 months postpartum using the Edinburgh Postnatal Depression Scale, Beck Depression Inventory and State-Trait Anxiety Inventory. Bifactor modelling with the individual questionnaire items produced a general affect factor score that provided an integrated measure of anxiety and depressive symptoms. Linear mixed models were used to model the sleep outcomes, with adjustment for maternal age, education, parity, ethnicity, sex of the child and maternal sleep quality concurrent with maternal mental health assessment. We found that poorer mid-pregnancy, but not postpartum, maternal mental health was associated with longer wake after sleep onset duration across the first year of life (β = 49, 95% confidence interval 13-85 min). Poor maternal mental health during mid-pregnancy is linked to longer period of night awakening in the offspring during infancy. Interventions that aim to improve maternal antenatal mental health should examine infant sleep outcomes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".