Bibliographic record
Abstract
BACKGROUND: Lucid dreams occur when a person becomes aware that he or she is dreaming while still in the dream state. Previous reports on the use of lucid dreaming in the treatment of nightmares do not contain adequate baseline data, follow-up data, or both. METHODS: A treatment of recurrent nightmares incorporating progressive muscle relaxation, guided imagery, and lucid dream induction is presented for 2 case studies. Three other cases were treated with lucid dream induction alone. The duration of the nightmares ranged from once every few days to once every few months. RESULTS: The procedures were effective in all 5 cases. A 1-year follow-up showed that 4 of the subjects no longer had nightmares and that 1 subject experienced a decrease in the intensity and frequency of her nightmares. CONCLUSIONS: The alleviation of recurrent nightmares in these 5 cases parallels the results reported by other authors who have used training in lucid dreaming to treat nightmares. Our results support the idea that treatments based on lucid dream induction can be of therapeutic value. Based on these and other case studies, it remains unclear whether the principal factor responsible for the alleviation of nightmares is lucidity itself, or the ability to alter some aspect of the dream.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".