Prevalence Rates of Frequent Dream Recall and Nightmares by Age, Gender and Sleep Duration in 16 Countries
Bibliographic record
Abstract
The present study aimed to describe the prevalence rates of frequent (i.e., at least weekly) dream recall and nightmares with consideration for differences in age, gender and sleep duration in 16 countries using equivalent assessment methods. The study sample included 15,854 participants (69.9% women) aged 18-99 years (M = 42.39, SD = 16.43) collected by the International COVID-19 Sleep Study collaboration, which used a unified online survey to collect data from May to November 2021 across 16 countries. Participants provided demographic information as well as self-reported estimates of their dream recall and nightmare frequency and sleep duration in 2021 and retrospectively for 2019. Frequent dream recall occurred in 54.0% of participants in 2021 and 51.1% in 2019. Frequent nightmares were reported by 11.0% of participants in 2021 and 6.9% in 2019. Ad hoc regression models found dream recall and sleep duration to have a linear relation, whereas nightmare frequency demonstrated a quadratic relation to sleep duration. Frequent dream recall and nightmare prevalence rates are reported for each of the 16 study countries by age, gender and sleep duration. This is the first multi-continent study to estimate frequent dream recall and nightmare prevalence, which both provides updated prevalence rates during the COVID-19 pandemic as well as extends existing knowledge to previously never studied countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".