An emerging framework for digital mental health design with Indigenous young people: a scoping review of the involvement of Indigenous young people in the design and evaluation of digital mental health interventions
Bibliographic record
Abstract
BACKGROUND: Indigenous young people worldwide possess unique protective factors that support wellbeing. However, they experience mental illness at higher rates than their non-indigenous counterparts. Digital mental health (dMH) resources can increase access to structured, timely, and culturally tailored mental health interventions by reducing structural and attitudinal barriers to accessing treatment. The involvement of Indigenous young people in dMH resource development is recommended, however, no guidelines exist on how this can best be facilitated. METHODS: A scoping review examining processes to involve Indigenous young people in developing or evaluating dMH interventions was conducted. Studies reported between 1990 and 2023 involving Indigenous young people aged 12-24 years, originating from Canada, the USA, New Zealand, and Australia, in the development or evaluation of dMH interventions were eligible for inclusion. Following a three-step search process, four electronic databases were searched. Data were extracted, synthesized, and described under three categories: dMH intervention attributes, study design, and alignment with research best practice. Best practice recommendations for Indigenous research and participatory design principles derived from the literature were identified and synthesised. Included studies were assessed against these recommendations. Consultation with two Senior Indigenous Research Officers ensured Indigenous worldviews informed analysis. RESULTS: Twenty-four studies describing eleven dMH interventions met inclusion criteria. Studies included formative, design, pilot, and efficacy studies. Overall, most included studies demonstrated a high degree of Indigenous governance, capacity building, and community benefit. All studies adapted their research processes to ensure that local community protocols were followed and most aligned these within an Indigenous research paradigm. Formal agreements regarding existing and created intellectual property and implementation evaluations were rare. Outcomes were the primary focus of reporting, with limited detailed descriptions of governance and decision-making processes or strategies for managing predictable tensions between co-design stakeholders. CONCLUSIONS: This study identified recommendations for undertaking participatory design with Indigenous young people and evaluated the current literature against these criteria. Common gaps were evident in the reporting of study processes. Consistent, in-depth reporting is needed to allow assessment of approaches for this hard-to-reach population. An emergent framework, informed by our findings, for guiding the involvement of Indigenous young people in the design and evaluation of dMH tools is presented. TRIAL REGISTRATION: Available via osf.io/2nkc6.
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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 | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".