Co-Design of a Digital Health Platform for Chronic Disease Management in Rural Settings Using a Person-Centered, Collaborative-Care Model: Protocol for a 3-Phase Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Chronic diseases represent a significant global burden, accounting for 85% of the total disease burden in Australia. This burden is particularly pronounced in rural areas, where chronic disease rates are higher, and access to health care services is more limited. Digital technology has the potential to address these disparities by overcoming challenges such as workforce shortages and geographic isolation. OBJECTIVE: Our objective is to develop a digital health platform (DHP) to support the monitoring and management of chronic disease in collaboration with rural and regional stakeholders, including researchers, health care providers, and patients. The platform is being designed to be flexible, enabling applications across a range of chronic health conditions relevant to rural contexts. METHODS: Guided by implementation science methodologies, we are adopting an evidence-based approach to developing a DHP for chronic disease management. Informed by co-design frameworks and best-practice guidelines, our development plan comprises three key phases: (1) stakeholder needs analysis, (2) co-design and platform development, and (3) postdesign evaluation and testing. The Federation University Human Research Ethics Committee (HREC Ref: 2023/169) granted ethics approval for this study. RESULTS: Data collection is underway. The phase 1 review has been completed, and we have 84 survey responses. Phase 2 has commenced, with 9 workshops and 2 interviews conducted to date. Phase 3 will not commence until phase 2 has been completed. At this stage, project completion is anticipated by late 2026. CONCLUSIONS: Findings will inform the desirability, feasibility, and acceptability of co-designed DHPs for chronic disease management in rural Australia. Further, the study will contribute to the evidence base on collaborative, context-sensitive digital health innovation for resource-limited populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77844.
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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 | 0.100 | 0.104 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".