The stay strong app as a self-management tool for first nations people with chronic kidney disease: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The high burden of chronic kidney disease in First Nations peoples requires urgent attention. Empowering people to self-manage their own condition is key, along with promotion of traditional knowledge and empowerment of First Nations communities. This study explores the potential of a culturally responsive tool, already found to have high acceptability and feasibility among First Nations people, to support self-management for First Nations people with kidney failure. The Stay Strong app is a holistic wellbeing intervention. This study explores the suitability of the Stay Strong app to support self-management as shown by the readiness of participants to engage in goal setting. Data were collected during a clinical trial which followed adaption of research tools and procedures through collaboration between content and language experts, and community members with lived experience of kidney failure. METHODS: First Nations (i.e., Aboriginal and Torres Strait Islander) participants receiving haemodialysis in the Northern Territory (n = 156) entered a three-arm, waitlist, single-blind randomised controlled trial which provided collaborative goal setting using the Stay Strong app at baseline or at 3 months. Qualitative data gathered during delivery of the intervention were examined using both content and thematic analysis. RESULTS: Almost all participants (147, 94%) received a Stay Strong session: of these, 135 (92%) attended at least two sessions, and 83 (56%) set more than one wellbeing goal. Using a deductive approach to manifest content, 13 categories of goals were identified. The three most common were to: 'connect with family or other people', 'go bush/be outdoors' and 'go home/be on country'. Analysis of latent content identified three themes throughout the goals: 'social and emotional wellbeing', 'physical health' and 'cultural connection'. CONCLUSION: This study provides evidence of the suitability of the Stay Strong app for use as a chronic condition self-management tool. Participants set goals that addressed physical as well as social and emotional wellbeing needs, prioritising family, country, and cultural identity. The intervention aligns directly with self-management approaches that are holistic and prioritise individual empowerment. Implementation of self-management strategies into routine care remains a key challenge and further research is needed to establish drivers of success.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".