Sleep Disturbance During Quarantine in the Era of the SARS-CoV-2 (COVID-19) Pandemic
Bibliographic record
Abstract
ABSTRACT Background Quarantine has been shown to affect sleep quality in previous analyses. However, a thorough investigation of the association between sleep disturbance and COVID-19 infection during quarantine is still lacking. Aim We aim to determine the impact of quarantine on sleep quality and such impact to anxiety. We also aim to investigate the use of medication and its impact on sleep quality during quarantine. Methods A cross-sectional study conducted in the Jazan region of Saudi Arabia during September 2020. The Pittsburgh Sleep Quality Index (PSQI) and the Generalised Anxiety Disorder Assessment (GAD-7) were used. Results The number of participants was 327, with an infection rate of 53.6% (n= 175). 60.8% (n=189) were quarantined. The mean PSQI score was 5.69 (SD=3.17), those who were quarantined had a higher score (M=6.33, SD=2.99) than those who were not (M=4.57, SD=3.23). After we control for the confounding of anxiety, the PSQI score was also higher in those quarantined (M=0.59, SD=0.26) than in those who were not (M=0.48, SD=0.31); t(120)=2.08, p<0.05. Zinc was noted to have a significant positive effect on sleep quality and anxiety level. Conclusion This analysis provides new insight into the effect of quarantine and anxiety levels on sleep quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".