Why Have Sleep Problems in Later-Midlife Grown Following the Great Recession? A Comparative Cohort Analysis
Bibliographic record
Abstract
OBJECTIVES: This research compares three cohorts of individuals in their fifth decade of life and examines whether sleep problems are greater in cohorts following the Great Recession. We argue that these differences will occur because postrecession cohorts are exposed to more economic burdens that harm sleep. We also suggest that postrecession exposure to economic burdens will be amplified among women, leading to greater cross-cohort differences in sleep problems. METHOD: Data were derived from the Health and Retirement Study, focusing on cohort surveys starting in 2004, 2010, and 2016 (N = 12,129). Structural equation models compared cohorts in latent levels of sleep problems and also examined whether economic burdens mediated cohort differences. Interactions tested whether cohort differences varied between men and women. RESULTS: The 2010 and 2016 cohorts had higher mean levels of sleep problems than the 2004 cohort. Greater postrecession exposure to economic burdens largely explained inter-cohort change in sleep problems, with this pattern stronger among women. DISCUSSION: Americans are approaching their senior years increasingly burdened by economic stressors that incur sleep problems. Practitioners and aging researchers should be prepared to address deleterious health consequences created by heightened sleep impairments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".