Wearables for early detection of atrial fibrillation and timely referral for Indigenous people ≥55 years: mixed-methods protocol
Bibliographic record
Abstract
INTRODUCTION: Digital health technologies have the potential to provide cost-effective care to remote and underserved populations. To realise this potential, research must involve people not traditionally included. No research focuses on the acceptability and feasibility of older Indigenous people using wearables for early atrial fibrillation (AF) detection. This protocol compares digital augmentation against standard practice to detect AF, evaluate heart health self-efficacy and health literacy changes and identify barriers in collaboration with Aboriginal Community Controlled Health Organisations. It will establish a framework for implementing culturally safe and acceptable wearable programmes for detecting and managing AF in Indigenous adults ≥55 years and older. METHODS: This mixed-methods research will use the Rambaldini model of collective impact, a user-centred, co-design methodology and yarning circles, a recognised Indigenous research methodology to assess the cultural safety, acceptability, feasibility and efficacy of incorporating wearables into standard care for early AF detection. ANALYSIS: Qualitative data will be analysed to create composite descriptions of participants' experiences and perspectives related to comfort, cultural safety, convenience, confidence, family reactions and concerns. Quantitative device data will be extracted and analysed via Statistical Product and Service Solutions (SPSS). CONCLUSION: Prioritising perspectives of older Indigenous adults on using wearables for detecting and monitoring cardiovascular disease will ensure that the findings are effective, relevant and acceptable to those impacted. ETHICS AND DISSEMINATION: Findings will be published in open-source peer-reviewed journals, shared at professional conferences, described in lay terms and made available to the public. The AHMRC HREC Reference Number approved 1135/15.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.091 | 0.020 |
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".