Culturally competent digital mental health platforms for youth and young adults: A systematic review
Bibliographic record
Abstract
Introduction Mental illness accounts for nearly half the disease burden among youth and is often linked to substance misuse, suicidal ideation, and self-harm. A major barrier to care is the lack of culturally and linguistically appropriate services, as mental health resources are largely shaped by Western, English-language frameworks. Culturally competent care, services aligned with patients’ social, cultural, and linguistic needs, may address this gap. This systematic review investigates core components of culturally competent digital mental health services globally. Methods A systematic search of MEDLINE, PsycINFO, Embase, and CINAHL for English-language articles was conducted, presenting primary research on digital mental health platforms targeting stress, anxiety, depression, or suicidal ideation in youth aged 10–30. Included platforms incorporated personalized features such as multilingual options or culturally relevant design. Two reviewers (SA, NA) independently screened titles and abstracts; shortlisted articles underwent full-text review. Data were extracted into a summary table covering study methods, platform features, and cultural adaptations. Quality was assessed using Hawker’s checklist. Results Out of 751 records, 12 studies met the criteria from Canada (3), Australia (3), Lebanon (2), China (1), Germany (1), USA (1), and Uganda (1). Most used qualitative methods (n=5) and examined mobile health apps (n=6). Culturally competent features included adapted interventions (n=4), language-specific tools (n=1), or both (n=7). Most studies showed improved mental health outcomes and enhanced access through culturally competent digital care. Conclusion Embedding culturally competent approaches into digital platform design—co-created with diverse youth—can reduce global mental health access barriers and promote healthcare equity.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".