Geocultural differences in preschooler sleep profiles and family practices: an analysis of pooled data from 37 countries
Bibliographic record
Abstract
STUDY OBJECTIVES: To examine (1) multidimensional sleep profiles in preschoolers (3-6 years) across geocultural regions and (2) differences in sleep characteristics and family practices between Majority World regions (Pacific Islands, Sub-Saharan Africa, Eastern Europe, Northeast Asia, Southeast Asia, South Asia, the Middle East and North Africa, and Latin America) and the Minority World (the Western world). METHODS: Participants were 3507 preschoolers from 37 countries. Nighttime sleep characteristics and nap duration (accelerometer: n = 1950) and family practices (parental questionnaire) were measured. Mixed models were used to estimate the marginal means of sleep characteristics by region and examine the differences. RESULTS: Geocultural region explained up to 30% of variance in sleep characteristics. A pattern of short nighttime sleep duration, low sleep efficiency, and long nap duration was observed in Eastern Europe, Northeast Asia, and Southeast Asia. The second pattern, with later sleep midpoints and greater night-to-night sleep variability, was observed in South Asia, the Middle East and North Africa, and Latin America. Compared to the Minority World, less optimal sleep characteristics were observed in several Majority World regions, with medium-to-large effect sizes (∣d∣=0.48-2.35). Several Majority World regions reported more frequent parental smartphone use during bedtime routines (Northeast Asia, Southeast Asia: 0.77-0.99 units) and were more likely to have electronic devices in children's bedroom (Eastern Europe, Latin America, South Asia: OR = 5.97-16.57) and co-sleeping arrangement (Asia, Latin America: OR = 7.05-49.86), compared to the Minority World. CONCLUSIONS: Preschoolers' sleep profiles and related family practices vary across geocultural regions, which should be considered in sleep health promotion initiatives and policies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".