Sleepless in inequality: findings from the 2018 behavioral risk factor surveillance system, a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Despite the large body of research on the adverse effects of income inequality, to date, few studies have examined its impact on sleep. The objective of this investigation is to examine the association between US state income inequality and the odds for regularly obtaining inadequate (< 7 h) and very inadequate (< 5 h) of sleep in the last 24 h. METHODS: We analysed data from 350,929 adults participating in the US 2018 Behavioral Risk Factor Surveillance System (BRFSS). Multilevel modeling was used to determine the association between state-level income inequality, as measured by the Gini coefficient, and the odds for obtaining inadequate and very inadequate sleep. We also determined if associations were heterogeneous across gender. RESULTS: A standard deviation increase in the Gini coefficient was associated with increased odds for inadequate (OR = 1.06, 95% CI: 1.00, 1.13) and very inadequate sleep (OR = 1.11, 95% CI: 1.03,1.20). Also, a cross-level Gini Coefficient X Gender interaction term was significant (OR = 1.07, 95% CI:1.01,1.13), indicating that increasing income inequality was more detrimental to women's sleep behavior. CONCLUSION: Future work should be conducted to determine whether decreasing the wide gap between incomes can alleviate the burden of income inequality on inadequate sleep in the United States.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".