0233 Sleep Quality and Disturbances, Emotional Regulation and Resiliency in Adolescents during the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Introduction The COVID-19 pandemic continues to evolve internationally, increasing levels of psychological stress in adolescents around the world, and thereby increasing their risk for emotional disorders associated with chronic stress. This ongoing threat to adolescents’ mental health requires that we identify factors that contribute to their ability to cope with situations shown to carry significant risks, such as the COVID-19 pandemic (i.e., their resiliency).Negative emotions are associated with chronic stress, and factors that reduce levels of negative emotions are associated with improved resiliency. Healthier sleep is associated with lower levels of negative emotions. Cognitive reappraisal (changing the way one thinks about potentially emotion-eliciting events) is an emotional regulation strategy that downregulates negative emotions. However, there is little information about the associations between sleep quality, emotional regulation, and resiliency in adolescents. The present study sought to fill this gap by examining the associations between adolescents’ sleep quality and disturbances, emotional regulation strategies and adolescents’ resiliency during the COVID-19 pandemic. Methods Forty-five adolescents (M=13.47, SD=1.7 years) participated in the study during the first wave of the COVID-19 pandemic in Canada (May 15 to June 30, 2020). The Pittsburgh Sleep Quality Index was used to assess adolescents’ self-reported sleep quality and disturbances. The Emotion Regulation Questionnaire was used to assess respondents' tendencies to regulate their emotions using cognitive reappraisal or expressive suppression. The Connor-Davidson Resilience Scale was used to measure resilience. Behavioral/emotional problems were assessed before the pandemic using the Youth Self Report (YSR). Results Hierarchical multiple linear regression analyses revealed that lower levels of sleep disturbances and frequent use of cognitive reappraisal to regulate emotions were associated with a higher level of resiliency during the COVID-19 pandemic, above and beyond the contributions of gender or pre-pandemic emotional or behavioral problems. Conclusion Better sleep quality and the habitual use of an emotional regulation strategy that is effective in downregulating negative emotions are associated with higher resiliency in adolescents facing the COVID-19 pandemic. The cross-sectional nature of the study does not allow the inference of causation. Support (If Any) CIHR 418638 to Reut GruberRGPIN-2015-04467 to Reut Gruber
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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".