Poor Quality of Sleep among Women during the perinatal period in Ethiopia: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Sleep is a very crucial physiological process for human beings. During pregnancy and the postpartum period, sleep becomes very vital and it needs additional total sleep time for a better pregnancy outcome. However, poor sleep quality remains a major public health concern, particularly for perinatal women. Therefore, the pooled prevalence and risk factors from the study will provide a more conclusive result to take evidence-based measures against poor sleep in perinatal women. Methods Ten published studies with a total of 4,297 participants were included. All appropriate databases and grey literature were searched to get relevant articles. Studies reporting the prevalence and associated risk factors of poor sleep quality among perinatal women were included. The quality of each study was assessed using the Newcastle-Ottawa quality assessment Scale (NOS). Data were extracted using Microsoft Excel 2010 and the analysis was done using STATA version 11 software. The pooled prevalence and its associated factors were determined using the random effect model. Heterogeneity between studies was evaluated using the I2 test. In addition, Publication bias was checked in subjective technique by funnel plot and using Egger’s statistical test. Results The pooled prevalence of poor sleep quality was 44.81% (95% CI = 32.29, 57.34; I2 = 99.1%). Depression ((POR) = 3.87: 95% CI: 1.09, 12.40; I2 = 0.0%) and third-trimester ((POR) = 4.09: 95% CI: 1.05, 15.39; I2 = 0.0%) were risk factors of poor sleep quality. Conclusion More than two-thirds of perinatal women were exposed to poor quality of sleep. This indicates poor quality of sleep is a high health burden in women during the perinatal period in Ethiopia. The Government should incorporate maternal mental health policy along with prenatal and postnatal health care services.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".