Sleep disturbance and its associated factors among pregnant women in Ethiopia: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction Globally, sleep disturbance is the foremost public health issue among pregnant women which might have undesirable birth outcome including neurocognitive impairment, preterm birth, low birth weight, and neonatal morbidity and mortality. In Ethiopia, inconsistent findings have been reported on the prevalence of sleep disturbance among pregnant women. Therefore, this review aims to estimate the pooled prevalence sleep disturbance and its associated factors among pregnant women in Ethiopia. Methods This systematic review and meta-analysis of observational studies was designed according to the PRISMA guideline. A systematic search of literature was conducted in PubMed, Scopus, Web of science, and Google Scholar using relevant searching key terms. The Newcastle-Ottawa scale was used to evaluate the quality of all selected articles. Data were analyzed using STATA Version 14 software. Publication bias was checked using Egger’s test and funnel plot. Cochran’s chi-squared test and I 2 values were used to assess heterogeneity. A fixed-effects model was applied during meta-analysis. Results In this review, six studies were included after reviewing 17,100 articles. The pooled prevalence of sleep disturbance among pregnant women in Ethiopia was 50.43% (95%CI: 39.34–61.52). Third trimester pregnancy AOR = 4.03; 95% CI: 2.84,5.71), multigravidity (AOR = 1.99; 95% CI: 1.54, 2.59), unplanned pregnancy (AOR = 2.56; 95% CI: 1.52,4.31), depression (AOR = 3.57; 95% CI: 2.04, 6.27), stress (AOR = 2.77; 95% CI: 1.57, 4.88), anxiety (AOR = 3.69; 95% CI: 1.42, 9.59) and poor sleep hygiene (AOR = 2.49; 95% CI: 1.56, 3.99) and were statistically associated with sleep disturbance among pregnant women. Conclusion This review revealed that the magnitude of sleep disturbance among pregnant woman in Ethiopia was relatively high and multiple factors determined the likelihood of having a disturbed sleep-awake pattern. Thus, the implementation of interventions for sleep disturbance after screening pregnant women is needed. Moreover, public health interventions targeted on the prevention of unintended pregnancy and depression during pregnancy should be implemented.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".