Young People’s Experiences Using a Digital Mental Health Tool to Support Their Care in a Real-World Service: Lived Experience–Led Qualitative Study
Bibliographic record
Abstract
Background: The uptake of digital mental health tools (DMHTs) in mental health services is suboptimal, limiting key avenues to facilitate personalized and measurement-based care. This misses critical opportunities for enhanced patient-clinician communication, improved assessment, and early intervention. Objective: This paper aims to understand young people's experiences and perceptions of engaging with a DMHT to support their care in a real-world setting over time. Methods: This study is part of a larger randomized controlled trial, where an added human support, the digital navigator (DN), provided technological and engagement assistance for young people to use a DMHT as part of usual care. The DN conducted 118 semistructured interviews with 73 young people (mean age 22.7, SD 2.7 y) at baseline and 3-, 6-, and 12-month follow-up visits. Results: We found that the majority of young people were enthusiastic about incorporating a DMHT into their care when they understood its potential to facilitate shared decision-making and enhance self-awareness of their mental health. Notably, the DN's support was effective in fostering this understanding at the initial stages of implementation. However, it was evident that the lack of clinician buy-in for using the DMHT posed a risk of disillusionment to young people's sustained engagement with the tool. Young people perceived that clinician uptake of the tool was poor, limiting its perceived value addition and sustainability. Conclusions: Young people want to use DMHTs in their care and DNs can effectively facilitate implementation through ongoing engagement and technical support. However, successful implementation of DMHTs also depends on broader systemic factors, particularly on clinician and service engagement. Future research should examine how to address these contextual barriers and optimize DN support for implementation and sustained engagement of DMHTs.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".