Sleep and Dreaming during a Pandemic: A Sample of Young Canadians
Bibliographic record
Abstract
The COVID-19 pandemic has led to profound changes in personal and professional activities, increased levels of stress, and negatively impacted general well-being. These factors and experiences are often related to lower sleep quality and can influence the emotional tone and content of dreaming. Here, we used an online survey to evaluate subjective changes in well-being, sleep quality and timing, as well as dream tone and content during the COVID-19 pandemic. Our results (n = 658) revealed significant changes in sleep timing since the onset of COVID-19, including later bedtimes and later morning waking times. Further, participants reported decreases in well-being and sleep quality, as well as increases in the intensity and negativity of dreams during the pandemic. Interestingly, about half (50.5 %) of the sample reported experiencing specific COVID-related content during dreaming with about a quarter (27.1%) experiencing this content as frequently as once a month or more. Together, our findings document the profound impact of the COVID-19 pandemic on general well-being, sleep, and dream content. Given the well-documented benefits of sleep, the changes in sleep patterns and quality noted here constitute a potential target to improve functioning and health status during periods of elevated stress (e.g., during the 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".