Estimation of sleep problems among pregnant women during COVID-19 pandemic: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To estimate the sleep problems among pregnant women during the COVID-19 pandemic. ELIGIBILITY CRITERIA: English, peer-reviewed, observational studies published between December 2019 and July 2021 which assessed and reported sleep problem prevalence using a valid and reliable measure were included. INFORMATION SOURCES: Scopus, Medline/PubMed Central, ProQuest, ISI Web of Knowledge and Embase. RISK OF BIAS ASSESSMENT TOOL: The Newcastle-Ottawa Scale checklist. SYNTHESIS OF RESULTS: Prevalence of sleep problems was synthesised using STATA software V.14 using a random effects model. To assess moderator analysis, meta-regression was carried out. Funnel plot and Egger's test were used to assess publication bias. Meta-trim was used to correct probable publication bias. The jackknife method was used for sensitivity analysis. INCLUDED STUDIES: A total of seven cross-sectional studies with 2808 participants from four countries were included. SYNTHESIS OF RESULTS: =0.19). Due to the probability of publication bias, the fill-and-trim method was used to correct the estimated pooled measure, which imputed four studies. The corrected results based on this method showed that pooled prevalence of sleep problems was 13% (95% CI 0% to 45%; p<0.001). Based on meta-regression, age was the only significant predictor of prevalence of sleep problems among pregnant women. LIMITATIONS OF EVIDENCE: All studies were cross-sectional absence of assessment of sleep problems prior to COVID-19, and the outcomes of the pregnancies among those with and without sleep problems in a consistent manner are among the limitation of the current review. INTERPRETATION: Pregnant women have experienced significant declines in sleep quality when faced with the COVID-19 pandemic. The short-term and long-term implications of such alterations in sleep on gestational and offspring outcomes are unclear and warrant further studies. PROSPERO REGISTRATION NUMBER: CRD42020181644.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".