Profiles and Predictors of Infant Sleep Problems Across the First Year
Bibliographic record
Abstract
OBJECTIVE: To identify profiles and predictors of maternal-reported infant sleep problems across the first postnatal year. METHODS: Survey data examining maternal mental and physical health, intimate partner violence (IPV), and infant sleep problems and night waking were gathered from a cohort of 1,460 nulliparous women at 15 weeks' gestation and when their infants were 3, 6, 9, and 12 months old. RESULTS: Latent class analysis revealed 5 profiles of infant sleep problems, including those who had few problems (24.7%), persistent moderate problems (27.3%), increased problems at 6 months (10.8%), increased problems at 9 months (17.8%), and persistent severe problems (19.4%). Persistent severe infant sleep problems were associated with prepartum and postpartum maternal depression (adjusted odds ratio [AOR] 2.13, 95% confidence interval [CI] 1.35-3.34, p < 0.01; AOR 2.52, 95% CI 1.64-3.87, p < 0.001, respectively), poorer prepartum and postpartum perception of health (adjusted mean difference [AMD] 23.48, 95% CI 24.9 to 22.1, p < 0.01; AMD 23.78, 95% CI 25.2 to 22.4, p < 0.001, respectively), increased postpartum anxiety (AOR 2.22, 95% CI 1.26-3.90, p < 0.01), and increased prevalence of IPV in the first year postpartum (AOR 1.86, 95% CI 1.20-2.87, p < 0.01). CONCLUSION: Poorer prepartum and postpartum maternal mental and physical health, and IPV, were associated with maternal report of persistent severe infant sleep problems. Women experiencing prenatal physical and mental health difficulties may benefit from advice on managing infant sleep and settling. Health professionals working with unsettled infants must be equipped to enquire about and respond appropriately to disclosures of IPV.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".