Sleep difficulties as a consistent risk factor for medically treated injuries among adolescents in 46 countries
Bibliographic record
Abstract
Adolescent poor sleep is common and has been associated with unintentional injury risks. Yet the comparability of evidence is limited by differences in measures of sleep and injury implemented across studies. We examined the potential cross-national consistency of relationships between poor sleep and unintentional injury using self-reports from 239 816 adolescents (50.8% girls) in 46 countries collected using a common survey procedure. A cross-sectional study was conducted using nationally representative records from the 2017/2018 International Health Behaviour in School-aged Children study. The prevalence of sleep indicators (difficulties in falling asleep, insufficient sleep, social jetlag) and annual medically treated injuries (any, multiple) were described cross-nationally and by gender. Multivariable modified Poisson regression analyses were conducted within and across countries to test the consistency of associations between sleep and injury. 16.3%-48.3% of adolescents reported an indicator of poor sleep and 44.0% sustained any injury. We observed striking cross-national variations in sleep, yet consistent gendered patterns across countries [e.g. sleep difficulties more prevalent among girls vs. insufficient sleep (non-school days) more prevalent among boys]. Country-level models displayed relatively consistent and positive associations. Multi-country (pooled) models demonstrated a consistency of effects, with the strongest association observed between difficulties in falling asleep and multiple injuries (prevalence ratio: 1.58, 95% CI: 1.55-1.61); these effects were especially pronounced in girls. Using standard indicators, this novel cross-national study demonstrated that poor sleep is a consistent risk factor for adolescent injuries. Given the recent epidemic of adolescent sleep problems, sleep hygiene represents a novel target for injury prevention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".