Nightmares and bad dreams: Their prevalence and relationship to well-being.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study, for the first time, distinguishes between nightmares and bad dreams, measures the frequency of each using dream logs, and separately assesses the relation between nightmares, bad dreams, and well-being. Eighty-nine participants completed 7 measures of well-being and recorded their dreams for 4 consecutive weeks. The dream logs yielded estimated mean annual nightmare and bad-dream frequencies that were significantly (ps < .01) greater than the mean 12-month and 1-month retrospective estimates. Nightmare frequency had more significant correlations than bad-dream frequency with well-being, suggesting that nightmares are a more severe expression of the same basic phenomenon. The findings confirm and extend evidence that nightmares are more prevalent than was previously believed and underscore the need to differentiate nightmares from bad dreams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it