Sleep During Pandemic Times: Summary of Findings and Future Outlook Through the Lens of the International <scp>COVID</scp> Sleep Study ( <scp>ICOSS</scp> )
Bibliographic record
Abstract
To study the impact of the COVID-19 pandemic on sleep and circadian rhythms-two fundamental pillars for health-the collaboration International COVID-19 Sleep Study (ICOSS) was established. The present overview comprehensively discusses the findings from this collaboration. Involving sleep researchers across the globe, ICOSS used a harmonised questionnaire to cover changes in sleep and sleep disorders, as well as physical and mental health. Two survey waves were conducted, one in 2020 and another one in 2021. In ICOSS-1, a total of 26,539 people from 14 countries across four continents (Europe, Asia, North and South America) participated. In ICOSS-2, two more countries joined ICOSS, and 15,813 people participated. The focus in ICOSS-2 was on Long COVID. Participants accessed the widely disseminated online surveys in their native language. In the 20 papers published so far, the surveys have uncovered several novel findings, including how the pandemic impacted sleep patterns, the prevalence of sleep disorders, chronotype-based differences and sleep-immune system interactions. To the best of our knowledge, there is no other large-scale multinational study targeting the general population investigating the role of sleep and sleep disorders alongside a variety of psychological, biological, social and economic factors during the recent COVID-19 pandemic.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".