P057 Prevalence of poor sleep in First Nations peoples across the globe: A systematic review
Bibliographic record
Abstract
Abstract This review aims to collate and assess the extant literature on the prevalence of sleep issues among First Nations peoples across the globe. A systematic literature search, was conducted across key academic databases and grey literature sources to retrieve studies published until November 2021. Studies offering quantitative data on sleep issues in First Nations Peoples were included. The NIH Quality Assessment Tool was used to assess the methodological quality and an adapted version of the Indigenous Quality Appraisal tool was used to evaluate First Nations' engagement in research. Results: A total of 65 studies,(28 longitudinal, 37 cross-sectional) met the inclusion criteria for this review. The included evidence indicated that the First Nations Australians reported night-time awakenings (22%), severe daytime sleepiness (27%), short sleep duration (35%) and long sleep duration (41%). The Māori population from New Zealand consistently reported insufficient sleep (36%) and short sleep duration (28.6%). The First Nations Americans / Alaska Native populations reported insomnia (25% to 33%) and insufficient sleep (15% to 40%). The Cree First Nations from Canada had a higher prevalence of insomnia (38.5%) and sleep deprivation (25% to 40%). Most studies scored "quality" in quality assessment. However, First Nations' engagement in research could not be adequately assessed due to limited information. The review findings highlight that a significant proportion of First Nations peoples are experiencing poor sleep. Considering the established link between poor sleep and adverse health outcomes, sleep health equity in First Nations communities should be a high priority for service providers and policymakers.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".