Involvement of Indigenous young people in the design and evaluation of digital mental health interventions: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Indigenous young people worldwide are at greater risk of developing mental health concerns due to ongoing inequity and disadvantage. Digital mental health (dMH) interventions are identified as a potential approach to improving access to mental health treatment for Indigenous youth. Although involvement in the development and evaluation of dMH resources is widely recommended, there is limited evidence to guide engagement of Indigenous young people in these processes. This scoping review aims to examine the methods used to involve Indigenous young people in the development or evaluation of dMH interventions. METHODS: Articles published in English, involving Indigenous young people (aged 10-24 years) in the development or evaluation of dMH interventions, originating from Australia, New Zealand, Canada and the USA will be eligible for inclusion. PubMed, Scopus and EBSCOhost databases (Academic Search Premiere, Computer and Applied Science complete, CINAHL, MEDLINE, APA PsychArticles, Psychology and Behavioural Sciences collection, APA PsychInfo) will be searched to identify eligible articles (from January 1990 onwards). Infomit and Google Scholar (limited to 200 results) will be searched for grey literature. Two reviewers will independently screen citations, abstracts and full-text articles. Study methods, methodologies, dMH intervention details, participant information and engagement, and dissemination methods will be extracted, analysed (utilising content analysis), and qualitatively assessed for alignment with best practice ethical guidelines for undertaking Indigenous health research. A narrative summary of findings will be presented. Reporting will follow the Consolidated Criteria for Strengthening Reporting of Health Research involving Indigenous peoples (CONSIDER) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines. DISCUSSION: To date, there are no reviews which analyse engagement of Indigenous young people in the development and evaluation of dMH interventions. This review will appraise alignment of current practice with best practice guidelines to inform future research. It will highlight appropriate methods for the engagement of young people in study processes, providing guidance for health practitioners, policy makers, and researchers working in the field of Indigenous youth and dMH. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework ( 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 | 0.029 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".