Thematic and Content Analysis of Idiopathic Nightmares and Bad Dreams
Bibliographic record
Abstract
STUDY OBJECTIVES: To conduct a comprehensive and comparative study of prospectively collected bad dream and nightmare reports using a broad range of dream content variables. DESIGN: Correlational and descriptive. SETTING: Participants' homes. PARTICIPANTS: Three hundred thirty-one adult volunteers (55 men, 275 women, 1 not specified; mean age = 32.4 ± 14.8 y). INTERVENTIONS: N/A. MEASUREMENT AND RESULTS: Five hundred seventy-two participants kept a written record of all of their remembered dreams in a log for 2 to 5 consecutive weeks. A total of 9,796 dream reports were collected and the content of 253 nightmares and 431 bad dreams reported by 331 participants was investigated. Physical aggression was the most frequently reported theme in nightmares, whereas interpersonal conflicts predominated in bad dreams. Nightmares were rated by participants as being substantially more emotionally intense than were bad dreams. Thirty-five percent of nightmares and 55% of bad dreams contained primary emotions other than fear. When compared to bad dreams, nightmares were more bizarre and contained substantially more aggressions, failures, and unfortunate endings. CONCLUSIONS: The results have important implications on how nightmares are conceptualized and defined and support the view that when compared to bad dreams, nightmares represent a somewhat rarer-and more severe-expression of the same basic phenomenon.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".