Towards A Socioeconomic Model of Sleep Health among the Canadian Population: A Systematic Review of the Relationship between Age, Income, Employment, Education, Social Class, Socioeconomic Status and Sleep Disparities
Bibliographic record
Abstract
A better understanding of the contribution of the socioeconomic status (SES) in sleep health could guide the development of population-based interventions aiming to reduce "the silent public health issue" that are sleep disturbances. PRISMA was employed to identify relevant studies having examined the association between social class, social capital, education, income/assets, occupation/employment status, neighborhood deprivation and sleep health. Sixteen cross-sectional and three longitudinal studies were selected, having sampled 226,029 participants aged from 2 months to 85 years old. Findings showed that: (1) sleep health disparities among children and adolescent are strongly correlated to parental socioeconomic indicators; (2) poor parental income, poor family SES and poor parental education are associated with higher sleep disturbances among children and adolescents; (3) lower education is a predictor of increased sleep disturbances for adults; (4) low SES is associated with high sleep disturbances in adults and old people and; (5) low income and full-time employment was significantly associated with short sleep among adults and old people. In conclusion, sleep health should be an important public health target. Such intervention would be beneficial for populational health, for all taxpayers and public administrations, which would see a reduction in absenteeism and productivity losses attributable to sleep-related health problems in the global economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".