The Use of a Digital Well-Being App (Stay Strong App) With Indigenous People in Prison: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Indigenous Australians in custody experience much greater rates of poor mental health and well-being than those of the general community, and these problems are not adequately addressed. Digital mental health strategies offer innovative opportunities to address the problems, but little is known about their feasibility in or impact on this population. OBJECTIVE: This study aims to conduct a pilot trial evaluating the impact of adding the Stay Strong app to mental health and well-being services for Indigenous women and men in custody. The trial compared immediate and 3-month delayed use of the app by the health service, assessing its effects on well-being, empowerment, and psychological distress at 3 and 6 months after the baseline. METHODS: Indigenous participants were recruited from 3 high-security Australian prisons from January 2017 to September 2019. The outcome measures assessed well-being (Warwick-Edinburgh Mental Wellbeing Scale), empowerment (Growth and Empowerment Measure [GEM]-giving total, 14-item Emotional Empowerment Scale, and 12 Scenarios scores), and psychological distress (Kessler Psychological Distress Scale). Intention-to-treat effects on these outcomes were analyzed using linear mixed models. RESULTS: Substantial challenges in obtaining ethical and institutional approval for the trial were encountered, as were difficulties in timely recruitment and retention due to staff shortages and the release of participants from prison before follow-up assessments and an inability to follow up with participants after release. A total of 132 prisoners (age: mean 33, SD 8 y) were randomized into either an immediate (n=82) or a delayed treatment (n=52) group. However, only 56 (42.4%) could be assessed at 3 months and 37 (28%) at 6 months, raising questions concerning the representativeness of the results. Linear improvements over time were seen in all outcomes (GEM total: Cohen d=0.99; GEM 14-item Emotional Empowerment Scale: Cohen d=0.94; GEM 12 Scenarios: Cohen d=0.87; Warwick-Edinburgh Mental Wellbeing Scale: Cohen d=0.76; Kessler Psychological Distress Scale: Cohen d=0.49), but no differential effects for group or the addition of the Stay Strong app were found. CONCLUSIONS: We believe this to be Australia's first evaluation of a digital mental health app in prison and the first among Indigenous people in custody. While the study demonstrated that the use of a well-being app within a prison was feasible, staff shortages led to delayed recruitment and a consequent low retention, and significant beneficial effects of the app's use within a forensic mental health service were not seen. Additional staff resources and a longer intervention may be needed to allow a demonstration of satisfactory retention and impact in future research. TRIAL REGISTRATION: ANZCTR ACTRN12624001261505; https://www.anzctr.org.au/ACTRN12624001261505.aspx.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".