Economic adversity and children’s sleep problems: Multiple indicators and moderation of effects.
Bibliographic record
Abstract
OBJECTIVE: Toward explicating relations between economic adversity and children's sleep, we examined associations between multiple indicators of socioeconomic status (SES)/adversity and children's objectively and subjectively derived sleep parameters; ethnicity was examined as potential moderator. METHODS: Participants were 276 third- and fourth-grade children and their families (133 girls; M age = 9.44 years; SD = .71): 66% European American (EA) and 34% African American (AA). Four SES indicators were used: income-to-needs ratio, perceived economic well-being, maternal education, and community poverty. Children wore actigraphs for 7 nights and completed a self-report measure to assess sleep problems. RESULTS: Objectively and subjectively assessed sleep parameters were related to different SES indicators, and overall worse sleep was evident for children from lower SES homes. Specifically, children from homes with lower income-to-needs ratios had higher levels of reported sleep/wake problems. Parental perceived economic well-being was associated with shorter sleep minutes and greater variability in sleep onset for children. Lower mother's education was associated with lower sleep efficiency. Children who attended Title 1 schools had shorter sleep minutes. Ethnicity was a significant moderator of effects in the link between some SES indicators and children's sleep. AA children's sleep was more negatively affected by income-to-needs ratio and mother's education than was the sleep of EA children. CONCLUSIONS: The results advocate for the importance of specifying particular SES and sleep variables used because they may affect the ability to detect associations between sleep and economic adversity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".