Targeted dream incubation and dream self-efficacy
Bibliographic record
Abstract
Abstract This preliminary study investigates the potential for a technique that enables purposeful guiding of dream content (Targeted Dream Incubation; TDI) to change the degree to which an individual feels in control of their dreams (Dream Self-Efficacy; DSE). DSE is a subset of a larger concept of self-efficacy relating to one’s belief in their own abilities and competencies. Examining DSE may be quite important, as past research has demonstrated that DSE may be linked to positive treatment outcomes in specific therapies, such as interventions for trauma-related nightmares. Furthermore, prior research has found that decreasing feelings of helplessness related to sleep has been shown to improve insomnia symptoms and daytime fatigue. Thus, our study sought to examine the relationship between TDI and DSE. We enrolled N = 25 participants in a TDI protocol conducted during a predominantly N1 sleep nap, where participants completed surveys before and after a TDI paradigm. Our results revealed that TDI was linked to DSE, with individuals reporting significantly higher levels of DSE after the TDI protocol. These results provide preliminary evidence for a technique (TDI) that could increase DSE with the overall aim of improving the efficacy of specific sleep-related interventions, such as treatments for trauma-related nightmares. Future research should aim to further confirm these results with a control condition and examine the effects of TDI within the context of behavioral sleep interventions.
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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 | 0.002 | 0.016 |
| 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) | 0.003 | 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".