Universal digital mental health interventions for children and youth: a scoping review
Bibliographic record
Abstract
Introduction: Digital mental health interventions (DMHIs) are increasingly used to support child and youth mental health, yet the scope and characteristics of universal (Tier 1) DMHIs remain poorly defined. Methods: This scoping review synthesized peer- reviewed studies of universal DMHIs delivered to children and youth aged 0-18 years. Results: Use of online programs and hybrid delivery were prevalent. Many programs used an independent structure, while facilitation varied across self- led, facilitator-led, and both-led models. Outcomes were commonly assessed across multiple domains, including emotional, behavioural, social, and cognitive outcomes. When single domains were examined, these most often focused on emotional outcomes such as anxiety and depression. Interventions frequently employed therapeutic approaches such as cognitive behavioural therapy and psychoeducation, with content emphasizing emotion regulation, coping and problem-solving skills, and mental health literacy. Structural features varied in length and number of sessions, and many included scaffolded self-managed elements such as messaging or check-ins. Early childhood was underrepresented, with limited reporting of child outcomes for ages 0-4. Equity considerations were also limited, as many studies did not report race or ethnicity and sex and gender reporting was often binary or unspecified. Youth involvement in intervention design or consultation was uncommon. Discussion: This review provides an overview of the current landscape and identifies key gaps, including limited equity considerations, the underrepresentation of early childhood development, and minimal youth involvement. Clearer reporting of delivery structure and facilitation, stronger demographic reporting, and component-focused evaluation can support equitable, developmentally responsive scale-up. Systematic Review Registration: https://inplasy.com/inplasy-2024-9-0026/, INPLASY202490026.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".