Cultural Adaptation of an Aboriginal and Torres Strait Islanders Maternal and Child mHealth Intervention: Protocol for a Co-Design and Adaptation Research Study
Bibliographic record
Abstract
BACKGROUND: There is limited evidence of high-quality, accessible, culturally safe, and effective digital health interventions for Indigenous mothers and babies. Like any other intervention, the feasibility and efficacy of digital health interventions depend on how well they are co-designed with Indigenous communities and their adaptability to intracultural diversity. OBJECTIVE: This study aims to adapt an existing co-designed mobile health (mHealth) intervention app with health professionals and Aboriginal and/or Torres Strait Islander mothers living in South Australia. METHODS: Potential participants include Aboriginal and/or Torres Strait Islander pregnant women and mothers of children aged 0-5 years, non-Aboriginal and/or Torres Strait Islander women who are mothers of Aboriginal and/or Torres Strait Islander babies, and health professionals who predominantly care for Aboriginal and/or Torres Strait Islander mothers and babies. Participants will be recruited from multiple Aboriginal and/or Torres Strait Islander-specific health services under the local health networks around metropolitan South Australia. In this study, data collection will be carried out via culturally safe, and family-friendly yarning circles, facilitated by Aboriginal research staff to collect feedback on the existing mHealth app from approximately 20 women and 10 health professionals, with the aim to achieve data saturation. This will inform the changes required to the mHealth app. All focus groups and interviews will be audio recorded and transcribed verbatim. Data will be inductively analyzed using realist epistemology via NVivo software (Lumivero). Themes about the mHealth app's cultural acceptability, usability, and appropriateness will be used to inform the changes applied to the app. RESULTS: With the feedback received from participating women and health professionals, changes in the smartphone app will be made to ensure the intervention is supportive and meets the needs of Aboriginal and/or Torres Strait Islander mothers and families in South Australia. Participation of community members will promote ownership, community engagement, and implementation. CONCLUSIONS: A co-designed, culturally sensitive, and effective digital health intervention is likely to support Indigenous mothers and their children facing health disparities due to the disruption of Indigenous culture by colaying a foundation for a potential clinical trial and wider implementation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53748.
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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 | 0.064 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.012 |
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".