Asymmetrical Effects of Sleep and Emotions in Daily Life
Bibliographic record
Abstract
= 5.06). They answered morning and evening questionnaires for 1 week at three different points in time separated by nine months each, 21 days in total. Measures of sleep quality and emotional experiences each morning were assessed, and they reported on their best and worst experience of the day, peak emotional responses to these events, and affect in the evening. Sleep behavior, including total sleep time and sleep efficiency, was objectively quantified using wrist actigraphy. Multilevel modeling analyses showed that longer sleep duration and better subjective quality predicted greater positive emotions and lower negative emotions upon waking, and lower levels of peak perceived stressfulness, but not peak positivity ratings. Daily experiences did not predict sleep duration. Conversely, negative affect in the evening and greater peak perceived stressfulness during the day predicted worse sleep quality that night, whereas positive affect and positive events were not related to sleep. Although correlational, these findings suggest that good sleep can improve waking affect and help mitigate the impact of stressful experiences but does not amplify responses to the positive events of the day. In turn, daily perceived stress reactivity impairs sleep quality. These novel findings show stronger bidirectional relationships between sleep with daily stress, than sleep with daily positivity. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00112-x.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
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".