Population-based studies highlighting the prevalence of sleep-related disorders in adults with respect to ethnicity
Bibliographic record
Abstract
Introduction: To eliminate disparities in health outcomes due to sleep disorders there is a need to not only focus on social and environmental factors, but also possible biological or genetic differences. This paper highlights studies investigating ethnic prevalence of certain sleep-related disorders in different populations. Methods: Recent studies focusing on ethnic differences in sleep-related disorders representing three different regions were selected. Results: A total of eight papers were reviewed; New Zealand (3), USA (3) and Singapore (2). Studies showed that there were significant differences between ethnic populations in certain sleep-related disorders. In New Zealand, studies showed Maori and Pacific Islanders have higher prevalence and were at higher risk for sleep-related disorders compared to Caucasians and Asians. In the USA, African-Americans showed higher prevalence for sleep-related disorders compared to Caucasians and Hispanics. As for Singapore, the studies compared three major ethnicities in South East Asia; Chinese, Indians and Malays. Studies showed that Chinese have the lowest prevalence and the lowest risk for sleep-related disorder compared to Indians and Malays. Conclusions: Differences in prevalence of sleep-related disorders with respect to ethnicity have implications in the development of treatment and services. There is a need for more consistent and reliable ethnic data for sleep-related disorders to enable the development and implementation of effective prevention, intervention and treatment.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".