Digital Conversational Agents for youth with multiple mental health conditions: a qualitative descriptive study of youth perspectives on the acceptability, potential benefits, and challenges
Bibliographic record
Abstract
Objective This qualitative descriptive study aimed to understand the perspectives of youth with multiple mental health conditions on the acceptability, benefits and challenges of digital conversational agents (i.e. ‘Conversational Agents’) for use in mental health care.Methods A total of 28 youth participated in one of six focus groups. Focus groups followed a semi-structured interview guide. Discussions were audio-recorded, transcribed verbatim, and analyzed using codebook thematic analysis. Polling questions were added to understand proportions of participants on discrete questions. Youth engagement was conducted throughout.Results Three themes were generated from the data: (1) Participants expressed a general initial willingness to use Conversational Agents for youth mental health support, with caution; (2) Participants recognised a wide range of practical benefits; and (3) The acceptability of was mitigated by significant risks and concerns.Discussion Conversational Agents hold potential as acceptable supportive tools for youth with multiple mental health conditions. However, caution should be exercised in developing tools that meet the needs of young people in terms of mental health support and ethical requirements..
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".