Co-Designing a Digital App to Support Young People’s Patient and Public Involvement and Engagement (VoiceIn): Development and Usability Study
Bibliographic record
Abstract
Background: While patient and public involvement and engagement (PPIE) is now seen as a cornerstone of mental health research, young people's involvement in PPIE faces limitations. Work and school demands and more limited independence can make it challenging for young people to engage with PPIE. Lack of ability or desire to attend face-to-face meetings or group discussions can further compound this difficulty. The VoiceIn app and digital platform were codeveloped by a multidisciplinary team of young people, mental health researchers, and software designers, and enables young people to engage directly with PPIE opportunities via a mobile app. Objective: This paper aims to describe how VoiceIn was developed through a series of co-design workshops with relevant stakeholders, specifically (1) how the initial design of VoiceIn was informed and driven by focus groups with young people, mental health professionals, and PPIE leads; (2) how VoiceIn was refined through collaboration with the aforementioned stakeholders; (3) the priorities for an app to support PPIE; (4) the key features necessary in the PPIE app; and (5) the recommended next steps in testing and deploying the digital platform. Methods: Initial co-design workshops took place with young people, mental health professionals, and PPIE leads to identify key features of an app to support PPIE. A series of VoiceIn design prototypes were developed and iterated based on the priorities and preferences of the stakeholders. The MoSCoW (must have, should have, could have, won't have) prioritization method was used throughout the process to identify priorities across the different stakeholder groups. Results: Co-design with young people, mental health professionals, and PPIE leads supported the successful development and improvement of the VoiceIn app. As a result of this process, key features were identified, including allowing for various modes of providing feedback (eg, polls and comments), reviewing project updates, and expressing interest in categories of research. The researcher platform was developed to support multimedia uploads for project descriptions; a jargon detector; a dedicated section for providing project updates; and a visually appealing, user-friendly design. While all stakeholder groups emphasized the importance of allowing app users to engage with the app in various ways and for there to be ongoing progress updates, group differences were also noticed. Young people expressed a desire for incentives and rewards for engaging with the app (eg, to post on their public social media profiles), and mental health professionals and PPIE leads prioritized flexibility in describing the project and its PPIE needs. Conclusions: A co-design approach was pivotal to the development of the VoiceIn app. This collaborative approach enabled the app to meet the divergent needs of young people, mental health professionals, and PPIE leads. This process mirrored the aspirations of PPIE initiatives by cocreating a digital health research tool with key stakeholders.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".