Prevalence and risk factors of sleep problems in Bangladesh during the COVID-19 pandemic: a systematic review and meta-analysis
Bibliographic record
Abstract
The outbreak of the novel coronavirus disease 2019 (COVID-19) has altered people's lives worldwide and fostered the emergence of sleep problems. However, no systematic review and meta-analysis has yet been conducted to rigorously evaluate the impact of COVID-19 on sleep problems from a Bangladeshi perspective. As a result, the current systematic review and meta-analysis aims to fill this knowledge gap, which may lead to a better understanding of the prevalence and risk factors associated with sleep problems. To conduct this systematic review, PRISMA guidelines were followed; a literature search was conducted to include studies published till 5th March 2022 from the inception of COVID-19 pandemic in Bangladesh searching databases such as PubMed, Scopus. A total of eleven studies were included. The JBI checklist was used to assess the methodological quality of included studies. The overall estimated prevalence of sleep problems was 45% (95% CI: 32% to 58%, I2 =99.31%). General populations were more affected by sleep problems [52% (95% CI: 36% to 68%, I2 =98.92%)] than the healthcare professionals [51% (95% CI: 23% to 79%, I2 =97.99%)] (χ2 = 137.05, p <0.001). Additionally, results suggested that suffering from sleep problems were higher among female (OR: 1.15; 95% CI: 1.03 to 1.29 compared to men); urban residents (OR: 1.77; 95% CI: 1.55 to 2.02 compared to rural); and anxious person (OR: 5.15; 95% CI: 4.32 to 6.14 compared to non-anxious), whereas single participants less likely to suffer from sleep related problems (OR: 0.81; 95% CI: 0.71 to 0.94). The prevalence rate of sleep problems was high and the general populations was at particularly high risk. Further longitudinal studies are warranted to investigate the trajectories of such sleep problems as a function of pandemic changes.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".