Recommendations for the development of digital conversational agents for youth with mental health conditions: A position paper
Bibliographic record
Abstract
Digital conversational agents ("chatbots") hold interesting potential as a technology that can be used to deliver mental health interventions and supports to people in need. Youth are high users of digital technology and are widely affected by mental health conditions; youth might therefore be particularly interested in using digital conversational agents to support their mental health. We reviewed the literature on digital conversational agents for mental health, then conducted a two-pronged qualitative study of these tools for youth with mental health conditions. While the evidence base is limited, there is some preliminary evidence of utility and acceptability to users of this technology. In our qualitative study, youth were cautiously optimistic about using a digital conversational agent for mental health and identified potential benefits, but also substantial concerns. In terms of content and format, they wanted reliable, accurate, validated information, a flexible format, and a friendly interaction style, with attention to confidentiality and security. From these findings, we propose eight distinct recommendations to guide the rigorous development of digital conversational agents for youth mental health. By following these recommendations, it may be possible to build tools that leverage the strengths and potential of modern conversational agents while mitigating the potential harms to youth.
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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 | 0.059 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".