Sleep health characteristics and positive mental health in Canadian youth: A cross-sectional analysis of the Health Behaviour in School-aged Children study
Bibliographic record
Abstract
OBJECTIVES: This study investigated the associations between specific sleep health characteristics and indicators of positive mental health among Canadian youth in grades 6-10. METHODS: We used cross-sectional data from the Canadian 2017/2018 Health Behaviour in School-aged Children study, a nationally representative sample of Canadian students. Our analyses included 14,868 participants (53.1% girls). We assessed the following self-reported characteristics of sleep health: nighttime insomnia symptoms, sleep duration, problems with daytime wakefulness, and weekend catch-up sleep. Positive mental health measures included self-reported life satisfaction, positive affect, self-efficacy, and self-confidence. Logistic regression models were used to assess associations while controlling for confounders. RESULTS: Participants who had no or little nighttime insomnia symptoms, who met sleep duration recommendations, who had no or rare daytime wakefulness problems, and who had no or little weekend catch-up sleep were more likely to report high life satisfaction (range of adjusted odds ratios=1.29-2.50), high positive affect (range of adjusted odds ratios=1.35-3.60), high self-efficacy (range of adjusted odds ratios=1.22-2.54), and high self-confidence (range of adjusted odds ratios=1.28-2.31). Almost all of the associations remained significant in the gender- and age-stratified analyses. CONCLUSION: The findings suggest that good sleep health is associated with higher odds of positive mental health among Canadian youth in grades 6-10. Further research is needed to understand the temporality of the associations and the underlying mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".