Socioeconomic Position and Excessive Daytime Sleepiness: A Systematic Review of Social Epidemiological Studies
Bibliographic record
Abstract
The objectives of this empirical study are to describe and discuss the current literature available on the relationship between excessive daytime sleepiness (EDS) and the socioeconomic position (SEP) as well as to provide recommendations for consideration of SEP in sleep medicine and biomedical research. Databases Medline/PubMed, Web of Science, Google scholar and Scopus were screened from January 1990 to December 2020 using PRISMA guidelines and 20 articles were included in the final synthesis. Nineteen studies were cross-sectional and one study was longitudinal. Among these studies, 25.00% (n = 5) are focused on children and adolescent and the remaining 75.00% (n = 15) focused on adults and seniors. Ages ranged from 8 to 18 years old for children/adolescent and ranged from 18 to 102 years old for adults. Main SEP measures presented in these studies were education, income, perceived socioeconomic status and employment. The sample size in these studies varied from N = 90 participants to N = 33,865 participants. Overall, a lower educational level, a lower income and full-time employment were associated with EDS. Symptoms of EDS are prevalent in women, especially those with a low income or no job; and children and adolescents with difficult living conditions or working part time reported more sleep disturbances. SEP is already considered as an important determinant for many health outcomes, but if SEP is embedded in the experimental design in psychosomatic research, biomedical research and clinical practice as a constant variable regardless of outcome; it will move forward future investigations.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".