The Acceptability of AI-Driven Resource Signposting to Young People Using a Mental Health Peer Support App
Bibliographic record
Abstract
Abstract Incorporating artificial intelligence (AI) into mental health applications (apps) can help to personalise support, for example through signposting topic-specific resources based on content that app users interact with. However, there is limited research exploring the acceptability of AI within digital mental health for young people. The current study explored this in the context of an online peer support platform for young people. 12 young people were interviewed online using a think aloud approach; they were aged 16–23 (M 18.64, SD 2.23). Participants identified as White ( n = 7), Chinese ( n = 1), Mixed Race ( n = 1), Indian ( n = 1), Black African ( n = 1) and Bangladeshi ( n = 1). 10 participants identified as women, one as non-binary and one preferred not to say. Participants were users of Tellmi, a pre-moderated mental health peer support app aimed at young people. Participants were given a link to a prototype of the Tellmi app via their web browser in which it was shown how AI could generate suggestions of pre-defined resources based on the content of fictional posts. Users were encouraged to interact with it whilst thinking aloud. Three themes were developed using reflexive thematic analysis: (1) Fear of the unknown - getting to grips with artificial intelligence; (2) AI can help save time and effort by streamlining processes; and (3) The value of human connection, which included the sub-theme: AI isn’t human and shouldn’t pretend to be.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".