P041 Poor Sleep and Mental Health Issues Among First Nations Peoples
Bibliographic record
Abstract
Abstract To evaluate the evidence on sleep and mental health issues in First Nations peoples. A systematic literature search was conducted covering academic and grey literature databases for studies with quantitative data on sleep and mental health association in First Nations Peoples published until November 2021. The National Institutes of Health Quality Assessment Tool was used for quality assessment, and the Aboriginal and Torres Strait Islander Quality Appraisal Tool helped assess cultural appropriate conduct of research. Seven studies (6 cross-sectional and 1 longitudinal) among three First Nations groups (n=3075) were included. In Indigenous Australian children, arousal problems increased aggression, and withdrawn behaviour, while early bedtime protected against behavioural problems (OR: 0.48, 95% CI: 0.28 -0.82). In Native American youth, insomnia symptoms increased depressive symptoms (OR: 4.87, 95% CI: 2.4 to 9.89), while in adults, short sleep increased the risk of anxiety (16%) and affective disorders (16%). Clinical sleep issues, restless leg (OR: 1.82; 95% CI: 0.53 to 3.12), insomnia (OR: 4.49; 95% CI: 3.14 to 5.83), and apnoea (OR: 2.46; 95%CI: 0.47 to 4.46) were associated with depression. Similarly, in Ameridian/Mestizo adults, restless leg syndrome increased the risk of depression (OR: 4.5, 95% CI: 2.2 to 9.7) and anxiety (OR: 3.6, 95% CI: 1.7 to 7.7). Majority of the studies scored high in quality assessment but the lack of information limited adequately assessing cultural appropriateness. There is limited but strong evidence suggesting a strong role of poor sleep in mental health issues in First Nations peoples which compels investment in sleep health.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".