Variety and Intensity of Emotions in Nightmares and Bad Dreams
Bibliographic record
Abstract
Nightmares are usually defined as frightening dreams that awaken the sleeper. This study uses the waking criterion to distinguish between nightmares and bad dreams and investigated the variety and intensity of emotions reported in each form of disturbing dream. Ninety participants recorded their dreams for 4 consecutive weeks and, for each dream recalled, noted the emotions present and their intensities on a 9-point scale. Thirty-six participants reported at least one nightmare and one bad dream over the 4 weeks covered by the log, while 29 reported having had at least one bad dream but no nightmares. Nightmares were rated as being significantly (p < 0.001) more intense than bad dreams. Thirty percent of nightmares and 51% of bad dreams contained primary emotions other than fear. The findings support the claim that awakening can serve as an indirect measure of nightmare intensity and raise important implications for the operational definition of nightmares.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".