Dream Recall Frequency, Lucid Dream Frequency, and Personality During the Covid-19 Pandemic
Bibliographic record
Abstract
Dream recall frequency and lucid dream frequency showed large inter-individual differences that are partly related to personality dimensions. However, as dream research is a small field, independent studies are necessary to build a solid empirical foundation. The present online survey included 1,537 participants (1150 women, 387 men) with a mean age of 35.1 ± 15.8 years. Whereas the relationship between openness to experience and dream recall frequency was in line with previous research - supporting the life-style hypothesis of dream recall, the associations between the Big Five personality factors and lucid dream frequency are less homogenous; for example, the negative relationship between neuroticism and lucid dream frequency. Even though the effect sizes of these associations are small, the findings can help in identifying links between waking and dreaming. Moreover, it was found that lucid dream frequency was related to Covid-19-related worries, whereas dream recall frequency was not.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".