Measuring nightmare and bad dream frequency: impact of retrospective and prospective instruments
Bibliographic record
Abstract
Studies on nightmare frequency have yielded inconsistent results. We compared the frequency of nightmares and bad dreams obtained with retrospective methods (annual and monthly estimates) and with two types of prospective measures (narrative and checklist logs). Four hundred and eleven participants completed retrospective estimates of nightmare and bad dream frequency and recorded their dreams in either narrative or checklist logs for 2-5 weeks. When measured prospectively with narrative logs, nightmare frequency was marginally higher than the 1-year estimate (P = 0.057) but not significantly different from the 1-month estimate (P > 0.05). Prospective bad dream frequency was significantly greater than the two retrospective estimates (ps < 0.0005). There were no significant differences in the frequency of nightmares and bad dreams reported prospectively with narrative versus checklist logs (ps > 0.05). However, checklist logs yielded a significantly greater number of everyday dreams per week (P < 0.0001). Taken together, the results provide partial support for the idea that when compared to daily logs, retrospective self-reports significantly underestimate current nightmare and bad dream frequency. Prospective studies of dream recall and nightmare frequency should take into account the type of log used, its duration, and the participants' level of motivation over time.
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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 | 0.047 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".