Trajectories of Sleep Quality and Associations with Mood during the Perinatal Period
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate trajectories of sleep quality and associations with mood in the perinatal period. Although it is commonly accepted that subjective sleep quality declines during pregnancy and the transition to parenthood, some women may follow qualitatively distinct trajectories. DESIGN, SETTING, AND PARTICIPANTS: Sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI). Data were collected from 293 women at four time points: during early pregnancy, at Time 1 (T1; < 22 w gestational age [GA]; late pregnancy, at Time 2 (T2; 32 w GA); during the postnatal period at Time 3 (T3; 3 mo postpartum); and Time 4 (T4; 6 mo postpartum). A group-based semiparametric mixture model was used to estimate patterns of sleep quality throughout the perinatal period. RESULTS: Four trajectory groups were identified, including patterns defined by high sleep quality throughout (21.5%), mild decrease in sleep quality (59.5%), significant decrease in sleep quality (12.3%) and a group with poor sleep quality throughout (6.7%). Women who had the worst sleep quality at Time 1 and those who experienced significant increases in sleep problems throughout pregnancy were also the groups who reported the highest levels of anxiety and depressive symptoms in early pregnancy and the lowest levels of social support. After controlling for covariates, the groups with worst subjective sleep quality during pregnancy were also the most likely to experience high symptoms of depression in the postpartum period. CONCLUSIONS: Most of the women in our sample reported mild sleep disturbances through the perinatal period. A subgroup of women reported a significant decline in sleep quality from early to late pregnancy and another reported poor subjective sleep quality throughout pregnancy; these groups had the greatest risk of experiencing high symptoms of depression in the postpartum period.
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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 | 0.001 |
| 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".