Digital Health Solutions for Indigenous Mental Well-Being
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes digital health solutions being used for Indigenous mental well-being, with emphasis on available evidence and examples reported in the literature. We also describe our own local experience with a rural telemental health service for Indigenous youth and discuss the unique opportunities and challenges. RECENT FINDINGS: Digital health solutions can be grouped into three main categories: (1) remote access to specialists, (2) building and supporting local capacity, and (3) patient-directed interventions. Limited evidence exists for the majority of digital solutions specifically in Indigenous contexts, although examples and pilot projects have been described. Telemental health has the strongest evidence, along with a growing evidence for web-based applications, largely led by Australia. Other digital approaches remain areas of promise requiring additional study. Co-design and service integration and respect for Indigenous history and ideologies are essential for success. While the use of digital health solutions for Indigenous mental well-being holds promise, there is a limited evidence base for most of them. Future efforts to expand the use of digital solutions in this population should adhere to best practices for the delivery of Indigenous health services.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".