0294 RESILIENCE, EMOTION AND AROUSAL REGULATION IN INSOMNIA DISORDER
Bibliographic record
Abstract
According to the diathesis-stress model of insomnia, a vulnerability to developing it may lead to insomnia in response to stress. Recently, there has been a paradigm shift in the understanding of resilience in context of stress- risk-vulnerability dimension. Resilience is a psychobiological factor which determines individual’s capacity to adapt successfully to stressful events. Lower level of resilience increases vulnerability for developing mental disorders. Beacuse emotion and arousal regulation is a key factor in insomnia the aim was to explore the level of resiliency in subjects with insomnia and its relationship with emotion and arousal regulation. The study consisted of 48 subjects with Insomnia disorder according to the DSM-5 and 35 good sleepers. Insomnia Severity Index (ISI), Resilience Scale for Adults (RSA), Difficulties in Emotion Regulation Scale (DERS), Pre-sleep Arousal Scale (PSAS) were administered while controlling for anxiety and depressive symptoms. Differences in means between groups were assessed using t-test or Mann-Whitney U/Wilcoxon test. Univariate/ multivariate regression analyses and mediation analyses were performed. Subjects with Insomnia (F 24, mean age 49 ± 2.1) presented higher ISI, RSA, DERS and PSAS scores than good sleepers (F 22, mean age 47.2 ± 1.2) (ISI: 15.7 ± 5.8 vs 5.1 ± 0.6, p<.01; RSA 96.2 ± 9.5 vs 45 ± 15, p<.01; DERS: 83.1 ± 3.3 vs 24.1 ± 12.1, p<.01; PSAS Cognitive 23.3 ± 11 vs 10 ± 0.6, p<.01, PSAS Somatic 16.1 ± 7 vs 10.2 ± 1.2, p<.01). After controlling for anxiety/depressive symptoms, low level of resiliency correlated to DERS Impulse control difficulties (B=0.5, p=0.008) DERS Limited emotion regulation strategies (B=0.35, p=0.008) and PSAS Cognitive (B=0.42, p=0.003). Impulse control difficulties mediated the relationship between low level of resilience and cognitive hyperarousal-PSAS Cognitive (Z=2.03, SE=0.08, p=0.04). Subjects with insomnia show low level of resilience that is considered a mechanism of successful adaptation to stressors. In insomnia, low level of resilience is related to emotional deregulation and to cognitive hyperarousal. In particular, impulse control difficulties may mediate the relationship between lack of resilience and cognitive hyperarousal in subjects with insomnia. If resilience helps to minimize the extent of pathogenesis in developmental process an early identification of vulnerable candidates should be useful for preventing insomnia development and mainteinance. no support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".