Investigating the association between sleep and aspects of mental health in children: findings from the Canadian Health Survey on Children and Youth
Bibliographic record
Abstract
INTRODUCTION: Sufficient sleep and good quality sleep are crucial aspects of children's healthy development. While previous research has suggested associations between sleep and positive mental health, few studies have been conducted in Canadian children. METHODS: This study used data from the 2019 Canadian Health Survey on Children and Youth. Parents of children aged 5 to 11 years (N = 16 170) reported on their children's sleep habits and mental health. Descriptive statistics were used to calculate means and percentages for sleep and mental health indicators. Logistic regression was used to compare mental health outcomes by meeting sleep duration recommendations (9-11 hours of sleep vs. < 9 or > 11 hours of sleep), sleep quality (difficulties getting to sleep) and having enforced rules for bedtime. RESULTS: Overall, 86.2% of children aged 5 to 11 years met sleep duration recommendations (9-11 hours of sleep), 90.0% had high sleep quality and 83.1% had enforced rules for bedtime. While 83.0% of children had high general mental health, mental health diagnoses were reported for 9.5% of children, and 15.8% of children required or received mental health care. High sleep quality was consistently associated with better mental health, enforced rules for bedtime were associated with some negative mental health outcomes and meeting sleep duration recommendations tended not to be associated with mental health outcomes. CONCLUSION: Sleep quality was strongly associated with mental health among children in this study. Future research should explore longitudinal associations between sleep and mental health in Canadian children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".