Digital Conversational Agents for the Mental Health of Treatment-Seeking Youth: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital conversational agents (or "chatbots") that can generate human-like conversations have recently been adapted as a means of administering mental health interventions. However, their development for youth seeking mental health services requires further investigation. OBJECTIVE: This youth-engaged scoping review synthesizes the recent research on digital conversational agents for youth seeking mental health or substance use services. METHODS: Studies were included if they were published between 2016 and 2025 and examined digital conversational agents for youth aged 11 to 24 years with mental health or substance use challenges in clinical settings. Systematic literature searches were conducted in February 2024 in multiple databases and updated in March 2025. Data were extracted using codeveloped forms and synthesized narratively. RESULTS: Ten studies were included, all focusing on mental health. Seven examined the acceptability and feasibility of digital conversational agents; others explored youth perceptions of use, design, and content, with some exploration of impact on mental health symptoms. Eight of ten studies reported high acceptability or positive user experiences. Three were randomized controlled trials that found potential reductions in depressive symptoms. Reporting on the ethical standards was limited. No studies focused on substance use alone. CONCLUSIONS: Literature on digital conversational agents for treatment-seeking youth is emerging but limited. Future rigorous research is needed that prioritizes data security, safety measures, and youth co-design in the development of safe, engaging, digital conversational agents for youth with mental health conditions.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".