Optimizing Digital Solutions to Improve Access to Comprehensive Primary Health Care Services in Remote Indigenous Communities: Protocol for a Participatory Action Research Project
Bibliographic record
Abstract
BACKGROUND: Aboriginal and Torres Strait Islander (Indigenous) peoples living in remote Australia experience a heavy burden of ill health and multiple barriers to accessing health care. Digital health technologies (DHTs) have the potential to help overcome some of these challenges and increase access to comprehensive primary health care (CPHC), thereby improving equity of health outcomes. However, little is known about the community and provider preferences for the use of digital technologies for improving health and wellness. OBJECTIVE: The study aims to co-design, implement, and evaluate how DHTs can improve access to CPHC in remote Indigenous communities in the Northern Territory (NT), Australia. METHODS: This multiphased project will take a participatory action research approach to co-design and optimize digital health solutions with local community members and health service staff in 2 communities. Our mixed methods approach will include pre- and postimplementation focus group discussions, interviews, quantitative analysis of CPHC utilization administrative data, and surveys administered by Indigenous community-based researchers to understand the use of digital devices and connectivity, eHealth literacy, preferences for different attributes of DHTs using best-worst scaling, and consumer satisfaction and experiences with DHT interventions. Priority DHTs will be selected for implementation based on consumer and health staff preferences. Focus group discussions and interview data will explore community and health service staff preferences, experiences, and satisfaction with implemented DHTs. A realist approach will be taken to identify how DHT interventions work, for whom, and in what circumstances, so that the understanding of why some interventions work while others do not is expanded. Economic analyses will be conducted to calculate the incremental costs and benefits of implemented DHT interventions. The scalability of digital health solutions will be tested in two additional communities. Project partners include key funding, service, and support agencies in the NT and nationally. RESULTS: As of November 2024, we have selected two implementation sites. Digital health initiatives are underway at the implementation sites, and evaluation activities are progressing. The initial findings from these sites have informed our scalability assessment in an additional two sites. CONCLUSIONS: Knowledge translation is integral to the study design, which involves partnering with consumers, CPHC service providers, and a range of key stakeholders to inform health service providers and policy makers about which DHTs work for which groups of consumers, and under what circumstances, to improve access to CPHC. This unique study will accommodate consumer and provider preferences regarding the use of DHTs to improve CPHC access and address the lack of knowledge about how to deploy digital solutions to best support CPHC in remote Indigenous Australia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68892.
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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 | 0.109 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.057 | 0.010 |
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".