Gender-specific estimates of sleep problems during the \nCOVID-19 pandemic: Systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary \nThe outbreak of the novel coronavirus disease 2019 (COVID-19) \nchanged lifestyles \nworldwide and subsequently induced individuals’ sleep problems. Sleep problems \nhave been demonstrated by scattered evidence among the current literature on \nCOVID-19; \nhowever, little is known regarding the synthesised prevalence of sleep \nproblems (i.e. insomnia symptoms and poor sleep quality) for males and females separately. \nThe present systematic review and meta-analysis \naimed to answer the important \nquestion regarding prevalence of sleep problems during the COVID-19 \noutbreak \nperiod between genders. Using the Preferred Reporting Items for Systematic Reviews \nand Meta-Analyses \nguideline and Newcastle–Ottawa \nScale checklist, relevant studies \nwith satisfactory methodological quality searched for in five academic databases \n(Scopus, PubMed Central, ProQuest, Web of Science , and EMBASE) were included and \nanalysed. The protocol of the project was registered in the International Prospective \nRegister of Systematic Reviews (PROSPERO; identification code CRD42020181644). \nA total of 54 papers (N = 67,722) in the female subgroup and 45 papers (N = 45,718) in \nthe male subgroup were pooled in the meta-analysis. \nThe corrected pooled estimated \nprevalence of sleep problems was 24% (95% confidence interval [CI] 19%–29%) \nfor female \nparticipants and 27% (95% CI 24%–30%) \nfor male participants. Although in both \ngender subgroups, patients with COVID-19, \nhealth professionals and general population \nshowed the highest prevalence of sleep problems, it did not reach statistical \nsignificance. Based on multivariable meta-regression, \nboth gender groups had higher \nprevalence of sleep problems during the lockdown period. Therefore, healthcare providers \nshould pay attention to the sleep problems and take appropriate preventive \naction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it