COVID-Somnia: A Multicentric Study on Sleep Disturbances During the COVID-19 Pandemic With Spatial Mapping of Hotspots
Bibliographic record
Abstract
Objective The purpose of this study was to document sleep quality and assess its sociodemographic, behavioral (i.e., tobacco use, alcohol use, and screen time), and mental-health-related indicators (i.e., anxiety and depression) in adults aged 30-59 years across three states of India, and to geo-locate state and district-level findings of sleep quality during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Methods From October 2020 to April 2021, residents (aged 30-59 years) of Kerala, Madhya Pradesh, and Delhi completed a web-based survey that included sociodemographic and behavioral factors, clinical history of COVID-19, and mental health screening instruments for anxiety and depression, namely the Generalized Anxiety Disorder 2-item (GAD-2) and Patient Health Questionnaire-2 (PHQ-2). The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the quality of sleep. Average PSQI scores were geo-mapped. Results Of the 694 participants who responded, 647 completed the PSQI. The mean (SD) global PSQI score was 5.99 (3.2), with approximately 54% of participants reporting poor sleep quality (PSQI Score>5). Eight hotspot districts with severe sleep disturbances (mean score PSQI>6.5) were identified. Multivariable logistic regression analysis showed that compared to Madhya Pradesh, participants from Kerala and Delhi had 62% and 33% lower chances of having poor sleep quality, respectively. Those who screened positive for anxiety had higher odds of having poor sleep quality (adjusted odds ratio {aOR}=2.4, P=0.006*). Conclusion Overall, sleep quality was poor during the early stages of the COVID-19 pandemic (October 2020-April 2021), especially among those who reported high levels of anxiety. Among the three included states, there were differences in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".