Factors Affecting Sleep Quality and Prenatal Distress Among Rural and Urban Women During Early Pregnancy
Bibliographic record
Abstract
Background Early pregnancy is characterized by the initiation of physiological and psychological changes, which places pregnant women at risk of psychological distress and poor sleep, which is known to cause adverse maternal and neonatal outcomes. This study aimed to assess the prevalence of prenatal distress and sleep quality during early pregnancy and identify factors associated with prenatal distress among pregnant women from urban and rural settings. Methods The study was conducted with 325 pregnant women (175 rural, 150 urban) as a baseline assessment of the MAI (Mother and Infant) cohort, a longitudinal observational study in Pune, India. Data on sociodemography, anthropometry, clinical history, prenatal distress, and sleep quality were collected between August 2020 and March 2023. Mann Whitney U test and regression were used to assess correlates of sleep quality and prenatal distress. Results Over one-third (37.5%) (n=122) of women experienced prenatal distress. Women from rural areas reported a higher prevalence (40%) (n=70) of distress, and poorer sleep quality than urban women (51.4% (n=90) vs 38.7% (n=58)). High prenatal distress was moderately associated with poor sleep quality (ρ = 0.308, p = 0.001). After controlling for sociodemographic and clinical factors, high prenatal distress (B=2.63, 95% CI: 1.47-4.69) predicted poor sleep quality. Rural residence (OR: 6.37, 2.46-16.51), underweight BMI status (OR: 2.21, 0.97-5.05), presence of episodes of vomiting (OR: 1.70, 0.93-3.13), and poor sleep quality (OR: 0.74, 0.40-1.38) significantly (p<0.05) contributed to prenatal distress. Conclusion Prenatal distress and poor sleep quality are significant concerns for pregnant mothers globally and require early screening and management strategies to avoid adverse maternal and fetal 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 | 0.001 |
| 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".