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Record W4284958195 · doi:10.1186/s12882-022-02856-x

The stay strong app as a self-management tool for first nations people with chronic kidney disease: a qualitative study

2022· article· en· W4284958195 on OpenAlexaboutno aff
Tricia Nagel, Kylie Dingwall, Michelle Sweet, David J. Kavanagh, Sandawana William Majoni, Cherian Sajiv, Alan Cass

Bibliographic record

VenueBMC Nephrology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsThematic analysisMedicineEmpowermentIntervention (counseling)Qualitative researchPromotion (chess)Kidney diseaseSelf-managementNursingFamily medicineEconomic growthSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.410
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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