INVESTIGATING DREAMING IN COGNITIVELY DIVERSE OLDER ADULTS
Bibliographic record
Abstract
Abstract Dreaming changes across the lifespan. While our understanding of how dreaming relates to aging and well-being has developed with respect to early and middle phases of the lifespan, the dreams of older adults remain under-studied. Reviews of the literature suggest decreases in dream recall and the emotionality of dream content that often precede age-related changes in sleep architecture, with few changes in dreaming occurring between ages 45-75, after which dream recall declines steeply. However, these findings are based on relatively few studies. This point is underscored when considering the effects of secondary aging on dreaming, where virtually no literature exists on the dreams of older adults experiencing mild cognitive impairment. Studying the dreams of older adults experiencing cognitive impairment may seem trivial when compared to other processes implicated in secondary aging, but recent studies examining the relationship between frequency of negative dream content and the increased risk of onset and acceleration of cognitive decline beg a closer examination of how dreaming contributes to a broader understanding of health and functioning across the lifespan. The scoping review of sleep, dreams, well-being and cognition across the lifespan, and the subsequent proposed study design seeks to employ the Hall and Van de Castle Coding System of content analysis to contribute to the literature on the dream frequency and content of cognitively typical older adults while also exploring the feasibility and methodological considerations of studying the dream content of older adults with mild cognitive impairment.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".