A co-designed program for better sleep in Australian First Nations adolescents: protocol for the Let’s Yarn About Sleep adolescent sleep health program
Bibliographic record
Abstract
The first-ever comprehensive report on the sleep health of Aboriginal and Torres Strait Islander peoples (hereafter referred to as First Nations Australians) highlighted an 18% prevalence of poor sleep in First Nations youth. While sleep health is important across the lifespan, adolescence is a critical life stage with increased vulnerability to poor sleep. In adolescents, pubertal changes, social and academic commitments, and peer pressure significantly increase the risk of poor sleep, which often results in social and emotional well-being (SEWB) issues. In First Nations adolescents, high rates of SEWB issues demand effective prevention and management strategies. Evidence from non-First Nations adolescents suggests that timely prevention, identification, diagnosis, and management of poor sleep help reduce the risk and severity of SEWB issues in First Nations adolescents. A research program is proposed to be called "Let's Yarn About Sleep," which will co-design, deliver, and evaluate a tailored sleep improvement program for Australian First Nations adolescents (12-18 years). Co-design workshops will be conducted with First Nations community Elders, parents and carers, youth, and First Nations service providers to develop the sleep health program. The program will also include training Aboriginal Youth Workers (AYWs) to deliver the sleep health program. The program evaluation will be based on a mixed methods design, using self-reported (survey tools and focus group discussions) and technology-based measures (actigraphy data) to measure changes in First Nations adolescents' sleep and SEWB. The evaluation will focus on the impact of training AYWs on program delivery and uptake.
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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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.076 | 0.016 |
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".