Effects of COVID-19 home confinement on sleep in children: A systematic review
Bibliographic record
Abstract
Our main aim was to examine the evidence of the effects of coronavirus disease confinement on the sleep of children aged 12 years and younger. A systematic review was conducted following the recommendations for Preferred Reporting Items for Systematic Reviews and Meta-Analyses. MEDLINE, Cumulative Index for Nursing and Allied Health Literature, Excerpta Medica Database, Psychological Information Database, and Web Of Science were systematically searched between the period of January 2020 and March 2021. The quality assessment was analysed with the Newcastle-Ottawa quality assessment scale and the National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies. The appraisal tool for cross-sectional studies was applied to cross-sectional studies and each longitudinal study was assessed with the critical appraisal skills programme. Data analysis was carried out through a narrative review. Eight studies were included in the review. Seven studies reported changes in sleep routines and five studies focused on sleep disturbances during confinement. The most important findings were a longer duration of sleep time, an increase in sleep latency, and daytime sleepiness. Whether or not the adverse changes to sleep patterns and bedtime routines seen during the home confinement period have any long-term consequences for children's sleep and daytime functioning remains unknown.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".