Sleep and circadian problems during the coronavirus disease 2019 (COVID‐19) pandemic: the International COVID‐19 Sleep Study (ICOSS)
Bibliographic record
Abstract
This protocol paper describes the development of an international collaboration to survey several thousand adults from different countries around the world about their sleep during the coronavirus disease 2019 (COVID-19) pandemic. It is based on the development of a harmonised survey with 50 questions (106 different items) on sleep habits and sleep symptoms that permit comparability of information. The harmonised questionnaire may be used in anonymous cross-sectional surveys, and the instruments within the questionnaire may also be used in prospective studies and clinical studies. The aim was to develop a questionnaire to sample a variety of sleep-wake disorders and other symptoms likely to be caused by prolonged social confinement or by having had COVID-19. The questionnaire was designed to be: (a) simple and, (b) free to use, for research purposes, (c) multilingual, and (d) comprehensive. It can be completed in <30 min. By the end of June 2020, the survey questionnaire had been administered in Austria, Canada, China, Finland, France, Germany, Hong Kong, Italy, Japan, Norway, Poland, Sweden, UK and USA. Research questions to be addressed by the pooled data derived from the participating sites focus on describing the nature and rates of various sleep and circadian rhythms symptoms, as well as their psychological and medical correlates, that arise at various points during the 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".