216 Resilience, sleep difficulties, and subjective sleep quality during COVID-19
Bibliographic record
Abstract
Abstract Introduction We have shown in a previous study that despite significant improvements in sleep patterns and sleep duration during COVID-19 in teens and young adults, only teens reported better sleep quality and satisfaction. Moreover, sleep difficulties seem to be more prominent in the older group during the pandemic, suggesting that there could be additional risk factors involved. The current study aimed to investigate the role of resilience in the association between changes in sleep and the subjective sleep quality (SSQ) in teens and young adults during COVID-19. Methods 289 teens (12-17 years old) and 294 young adults (18-25 years old) completed the Connor-Davidson Resilience Scale-10 and an adapted version of the Pittsburgh Sleep Quality Index online. Teens and young adults were each divided into a resilient and less resilient group. Hierarchical regression models were conducted to examine the unique contribution of weekdays sleep duration, sleep difficulties, and resilience to SSQ. Sleep duration, sleep difficulties and SSQ before COVID-19, and gender were entered as controls. Results Results show that in less resilient teens, changes in sleep onset difficulties (β=-.285, p=.003), nocturnal and early awakenings (β=-.218, p=.019), and weekdays sleep duration (β=.282, p=.001) significantly predicted SSQ and explained 36.5% of the variance. In less resilient young adults, changes in nightmares (β=-.309, p=.027) and sleep onset difficulties (β=-.263, p=.012) significantly predicted SSQ and explained 24.1% of the variance. In resilient teens, changes in weekdays sleep duration (β=.296, p=.007) significantly predicted SSQ and explained 20.1% of the variance. In resilient adults, changes in sleep onset difficulties (β=-.325, p=.001), nocturnal and early awakenings (β=-.374, p=.000), and weekdays sleep duration (β=.192, p=.009) significantly predicted SSQ and explained 46.0% of the variance. Conclusion Our results suggest that resilience appears to be a protecting factor in the impacts of sleep difficulties on sleep quality, but only in adolescents. Indeed, in young adults, sleep difficulties seem to be a more important factor modulating sleep quality than changes in sleep duration. These results underline the importance of focusing on the intrinsic characteristics of each population to better target interventions. Support (if any):
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".