Socioeconomic Status and Childhood Sleep: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Context: Socioeconomic status (SES) is increasingly understood to be a key contributor to sleep health, but the research in childhood has not been synthesized.Objective: To examine the associations between indicators of child SES and child sleep (≤18 years old), we conducted a systematic review and meta-analyses. Data Sources: CINAHL with Full Text, PsycINFO, and MEDLINE/PubMed were searched using terms to define SES and childhood to ascertain all relevant, peer-reviewed articles from database inception to 27 December 2019.Study Selection: Studies were included if an association between an indicator of parental SES and a measure of child sleep (duration, quality, and problems) was reported. Data Extraction: Data was extracted from 46 studies (N=72,915). Across these studies, total sample size across participants included in the meta-estimate was N=69,373. Results: Higher parental education was associated with longer childhood sleep duration (stronger in samples with a higher proportion of White children) and better sleep quality. Parental education was not directly associated with child sleep problems; moderation occurred by continent and the relationship was more pronounced in the Asian meta-estimate. Higher household income was not directly associated with longer childhood sleep duration, but moderation occurred by higher quality studies and the proportion of White children in the sample. Higher household income was associated with fewer sleep problems (moderated by continent) and higher sleep quality. Limitations: This review was limited by the number and methods of available published studies meeting inclusion criteria.Conclusions: Preventative programs that emphasize improvements in sleep of children and adolescents growing up in lower SES families are needed.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".