Bibliographic record
Abstract
Insufficient sleep has been associated with deficits in emotion processing; sleepy individuals show increased emotional reactivity and decreased emotion regulation. Individual differences that predict performance after sleep loss has remained largely elusive. Concentrations of cortisol, progesterone, and testosterone are candidate predictors for variability in performance following sleep loss. These hormones are associated with emotion functioning under well-rested conditions and show interactions with sleep and circadian rhythms. The central aim of this dissertation was to investigate the interaction of natural sleep and hormones on measures of emotion functioning. Study 1 examined the role of cortisol in the relationship between sleep (across the first three years of university), and self reported emotion functioning in undergraduate students. Poor sleep was associated with worse emotion regulation and reactivity, and greater concentrations of cortisol and cortisol/DHEA-S. Consistently poor sleepers over three years, who had high cortisol, experienced the greatest difficulties with emotion regulation. Study 2 investigated the association between sleep satisfaction and objective measures of sleep on self-reported emotional functioning in a group of children and adolescents. Importantly, in girls who were dissatisfied sleepers, being further though puberty was associated with the greatest difficulties with emotion regulation. Study 3 examined natural sleep, hormones, and menstrual phase on processing emotional stimuli. Participants completed sleep diaries and wore actigraphy watches for 3-weeks and completed measures of emotion perception on two occasions in the laboratory, in different menstrual phases for women. The study supported dynamic relationships between hormone concentrations and various measures of sleep duration and quality on the processing of emotion stimuli. Many relationships emerged for threatening emotions, indicating that high concentrations of testosterone, progesterone or cortisol, combined with poor sleep resulted in increased sensitivity towards threat detection. Together these studies provide evidence that hormones are an important factor in understanding the link between poor sleep and emotion functioning. Hormone concentration plays a role in understanding individual differences in response to sleep loss and can compound with sleep loss to result in worse emotional outcomes. Consideration of hormonal factors may help identify certain at-risk populations for sleep related deficits or timing of 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".