Expanding our understanding of digital mental health interventions for Indigenous youth: An updated systematic review
Bibliographic record
Abstract
Past research has examined available literature on electronic mental health interventions for Indigenous youth with mental health concerns. However, as there have recently been increases in both the number of studies examining electronic mental health interventions and the need for such interventions (i.e. during periods of pandemic isolation), the present systematic review aims to provide an updated summary of the available peer-reviewed and grey literature on electronic mental health interventions applicable to Indigenous youth. The purpose of this review is to better understand the processes used for electronic mental health intervention development. Among the 48 studies discussed, smoking cessation and suicide were the most commonly targeted mental health concerns in interventions. Text message and smartphone application (app) interventions were the most frequently used delivery methods. Qualitative, quantitative, and/or mixed outcomes were presented in several studies, while other studies outlined intervention development processes or study protocols, indicating high activity in future electronic mental health intervention research. Among the findings, common facilitators included the use of community-based participatory research approaches, representation of culture, and various methods of motivating participant engagement. Meanwhile, common barriers included the lack of necessary resources and limits on the amount of support that online interventions can provide. Considerations regarding the standards and criteria for the development of future electronic mental health interventions for Indigenous youth are offered and future research directions are discussed.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".