The relationship between social fragmentation and sleep among adolescents living in Boston, Massachusetts
Bibliographic record
Abstract
BACKGROUND: Sufficient sleep is needed for the healthy development of youth. However, only a small minority of adolescents obtain adequate amounts of sleep. Although individual-level correlates of sleep have been identified, studies investigating the influence of the environment on sleep are warranted. METHODS: By using cross-sectional data collected from 1878 urban adolescents living in 38 neighborhoods participating in the 2008 Boston Youth Survey (BYS), we determined the association between neighborhood social fragmentation and sleep. Social fragmentation of each participant's residential neighborhood was composed using 2010 US Census data. Multilevel regression models were used to determine the association between social fragmentation and meeting the recommended hours of sleep (>8.5 h) and sleep duration while controlling for individual-level sex, race, age and nativity. RESULTS: Moderate (OR = 0.51, 95% CI = 0.27, 0.97) and high (OR = 0.33, 95% CI = 0.18, 0.61) social fragmentation within the residential neighborhood was associated with a decreased likelihood of obtaining adequate sleep. Those in moderate (β = -23.9, 95% CI = -43.1, -4.8) and high (β = -22.1, 95% CI = -43.3, -0.9) socially fragmented neighborhoods obtained fewer minutes of sleep per night. CONCLUSIONS: Social fragmentation may be an important determinant of sleep among youth living in urban settings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".