Role of Stress, Arousal, and Coping Skills in Primary Insomnia
Bibliographic record
Abstract
OBJECTIVE: Although stress is often presumed to cause sleep disturbances, little research has documented the role of stressful life events in primary insomnia. The present study examined the relationship of stress and coping skills, and the potential mediating role of presleep arousal, to sleep patterns in good sleepers and insomnia sufferers. METHODS: The sample was composed of 67 participants (38 women, 29 men; mean age, 39.6 years), 40 individuals with insomnia and 27 good sleepers. Subjects completed prospective, daily measures of stressful events, presleep arousal, and sleep for 21 consecutive days. In addition, they completed several retrospective and global measures of depression, anxiety, stressful life events, and coping skills. RESULTS: The results showed that poor and good sleepers reported equivalent numbers of minor stressful life events. However, insomniacs rated both the impact of daily minor stressors and the intensity of major negative life events higher than did good sleepers. In addition, insomniacs perceived their lives as more stressful, relied more on emotion-oriented coping strategies, and reported greater presleep arousal than good sleepers. Prospective daily data showed significant relationships between daytime stress and nighttime sleep, but presleep arousal and coping skills played an important mediating role. CONCLUSIONS: The findings suggest that the appraisal of stressors and the perceived lack of control over stressful events, rather than the number of stressful events per se, enhance the vulnerability to insomnia. Arousal and coping skills play an important mediating role between stress and sleep. The main implication of these results is that insomnia treatments should incorporate clinical methods designed to teach effective stress appraisal and coping skills.
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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 | 0.002 |
| 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".