Obstructive sleep apnea diagnosis and management in First Nations communities: protocol for the Let’s Yarn About Sleep-Obstructive Sleep Apnea Program
Bibliographic record
Abstract
Abstract Obstructive Sleep Apnea (OSA) is a highly prevalent, yet significantly under-recognized disorder in First Nations Australians. Responding to strong community demand for local capacity building for sleep health equity, this paper outlines the Let’s Yarn About Sleep-OSA (LYAS-OSA) program protocol. The LYAS-OSA program will involve the co-design, implementation, and evaluation of a place-based, culturally responsive, nurse-led, and Aboriginal Health Worker-supported model for OSA diagnosis and management for First Nations peoples. This program will partner with health services and organizations across 12 communities in Queensland, Australia. The program will be conducted from 2023 to 2027. During the set up and development stage, an advanced data analytics study of secondary data will examine OSA phenotypes and symptomatology in First Nations Queensland communities. In addition, consumers and healthcare professionals will be engaged in co-design workshops to inform the development of a service delivery model framework. In stage two, local capacity building activities for Aboriginal Health Workers and nurses will be undertaken, with training on OSA diagnosis and management. This work will culminate in delivering and evaluating the co-designed service model. This community-led approach to co-designing, implementing, and evaluating the LYAS-OSA service delivery model will advance knowledge to deliver culturally responsive, context-responsive, OSA diagnosis, and management care for First Nations communities. The LYAS-OSA program outputs will significantly contribute to the evidence base and service delivery provision for OSA care, thereby improving sleep health equity for First Nations Australians. Statement of Significance Obstructive Sleep Apnea (OSA) in First Nations communities is highly prevalent, yet limited community awareness, lack of culturally responsive services, and unavailability of local diagnosis and management hinder timely and effective care. Addressing these gaps is crucial for improving sleep health equity for First Nations Australians. The program offers a community-led, co-designed, place-based model of care that integrates data analytics, co-design, and healthcare providers’ capacity building to address service delivery gaps. This approach aims to bridge service delivery gaps in Obstructive Sleep Apnea care for First Nations peoples across Australia. The outputs and outcomes from this program will significantly contribute to the evidence base for improving the quality and accessibility of Obstructive Sleep Apnea care for First Nations Australians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".