Using the Stay Strong App for the Well-being of Indigenous Australian Prisoners: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The gap between mental health needs and resources for Aboriginal and Torres Strait Islander people, the Indigenous people of Australia, is most marked in the prison population. Indigenous people are overrepresented in Australian prisons. In prison, this group experiences mental disorders to a greater degree than non-Indigenous prisoners. This group has also been found to experience mental disorder at a higher rate than Indigenous people in the community. In addition to pre-existing determinants of poor mental health, these high prevalence rates may reflect poor engagement in mainstream interventions or the efficacy of available interventions. In community populations, the use of digital mental health resources may help to increase access to well-being support. However, culturally appropriate digital tools have not been available to Indigenous people in prisons. The absence of feasibility and efficacy studies of these tools needs to be addressed. OBJECTIVE: The aim of this study is to determine the feasibility of the Stay Strong app as a digital well-being and mental health tool for use by Indigenous people in prison. METHODS: Dual government agency (health and corrective services) precondition requirements of implementation were identified and resolved. This was essential given that the Stay Strong app was to be delivered by an external health agency to Indigenous prisoners. Then, acceptability at a practice level was tested using postuse qualitative interviews with clients and practitioners of the Indigenous Mental Health Intervention Program. All Indigenous Mental Health Intervention Program practitioners (10/37, 27%) and client participants who had completed their second follow-up (review of the Stay Strong app; 27/37, 73%) during the study period were invited to participate. RESULTS: Owing to the innovative nature of this project, identifying and resolving the precondition requirements of implementation was challenging but provided support for the implementation of the app in practice. Acceptability of the app by clients and practitioners at a practice level was demonstrated, with nine themes emerging across the interviews: satisfaction with the current Stay Strong app, supported client goal setting, increased client self-insight, improved client empowerment, cultural appropriateness, enhanced engagement, ease of use, problems with using an Android emulator, and recommendations to improve personalization. CONCLUSIONS: The Stay Strong Custody Project is a pioneering example of digital mental health tools being implemented within Australian prisons. Using the app within high-security prison settings was found to be feasible at both strategic and practice levels. Feedback from both clients and practitioners supported the use of the app as a culturally safe digital mental health and well-being tool for Aboriginal and Torres Strait Islander people in prison.
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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 | 0.005 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".