A systematic review of electronic mental health interventions for Indigenous youth: Results and recommendations
Bibliographic record
Abstract
Electronic health interventions involve health services delivered using the Internet and related communication technologies. These services can be particularly relevant for Indigenous populations who often have differential access to health-care services compared to general populations, especially within rural and remote areas. As the popularity of electronic health interventions grows, there is an increased need for evidence-based recommendations for the effective use of these technologies. The current study is a systematic review of peer-reviewed and available grey literature with the aim of understanding outcomes of electronic health interventions for mental health concerns among Indigenous people. Studies used electronic health technologies for substance use treatment or prevention, suicide prevention, parenting supports, goal setting and behaviour change and consultation services. Various technological platforms were used across interventions, with both novel and adapted intervention development. Most studies provided qualitative results, with fewer studies focusing on quantitative outcomes. Some preliminary results from the engagement of Indigenous individuals with electronic health services has been demonstrated, but further research is needed to confirm these results. Identified barriers and facilitators are identified from the reviewed literature. Recommendations for future development of electronic health interventions for Indigenous youth are provided.
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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 | 0.024 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".