Sleep and next-day subjective cognition: stress mediation analysis
Bibliographic record
Abstract
Sleep duration and quality have been linked to cognition and cognitive performance throughout the literature (e.g. Lo et al., 2016; Hocket et al., 2021); however, less is known about sleep’s relationship with subjective cognition (i.e., one’s perception of their own cognitive abilities). Poor sleep quality and duration has been related to increased cognitive complaints (e.g. Gamaldo et al., 2019; Lin et al., 2022). Some work has additionally found poor sleep quality to be associated with decreased perceived cognitive abilities. For example, a study of shift-based nurses found that lower sleep quality and less time in bed was associated with decreased next-day subjective cognitive functioning (Veal, Mu, Small, & Lee, 2021), and that participants who reported higher sleep quality and duration overall tended to report better average cognitive performance (Veal, Mu, Small, & Lee, 2021). There is currently a paucity of research evaluating subjective attention on a daily level, and the daily relationship between sleep and attentional processes. As well, the mechanism underlying this relationship has yet to be rigorously assessed in daily life. Due to the previously established day-to-day relationships between sleep and next-day stress (e.g., Wen et al., 2021), as well as stress and subjective cognitive performance (e.g., Joshi, Vigoureux, & Lee, 2022), it is possible that psychological stress may be an important factor in the relationship between sleep and subjective cognition. The present study aims to evaluate stressor occurrence, affective reactivity, and rumination as possible mediators of the relationship between previous night’s sleep (quality and duration) and subjective attention and memory.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".