Exploring Youth Perspectives on Digital Mental Health Platforms: Qualitative Descriptive Study
Bibliographic record
Abstract
Background: The increasing prevalence of mental health disorders among youth underscores the need for accessible and effective interventions. Digital mental health (dMH) platforms like Innowell offer promising solutions by increasing access to mental health care for young people. Innowell is a web-based platform that supports youth mental health by providing personalized measurement-based care in collaboration with a youth's health care providers. However, understanding youth perspectives on these platforms is crucial for ensuring successful implementation and sustained engagement. Objective: This study aimed to explore youth perspectives on the implementation of the Innowell platform, identifying key factors influencing uptake, engagement, and long-term retention. Methods: A qualitative descriptive approach was used to examine youth perspectives. Data were collected through 9 focus groups and 1 interview, involving 39 participants aged 15-24 years from urban (23/39, 59%) and rural (16/39, 41%) communities in Alberta, Canada. Participants were recruited through mental health clinics and community organizations. Thematic analysis was conducted on the transcripts to identify factors that support or hinder engagement with the platform. Results: Participants emphasized the importance of privacy, security, and personalization in building trust in the platform, with 72% (28/39) reporting that clear communication about data protection would increase their likelihood of use. Progress tracking features, such as symptom trend visualizations and diaries, were identified by 65% (25/39) of participants as critical for sustaining engagement. Ease of use was highlighted, with 58% (23/39) preferring mobile app functionality over web-based interfaces. Dynamic content and personalized notifications were suggested as strategies to maintain long-term use, with 64% (25/39) of participants valuing customizable reminders to encourage daily interactions. Rural participants (16/39, 41%) noted the need for offline functionality due to inconsistent internet access. In addition, participants recommended features such as crisis support, professional communication channels, and access to local mental health resources. Conclusions: Youth-centered design is essential for enhancing the usability and engagement of dMH platforms like Innowell. Key features prioritized by participants included privacy, security, progress tracking, and personalization. Dynamic and user-friendly interfaces, along with the ability to customize notifications and access professional support, were critical for fostering long-term engagement. Insights from this study provide actionable recommendations for optimizing dMH platforms to meet the mental health needs of young people, particularly in diverse urban and rural settings. Future research should explore implementation strategies tailored to specific user demographics to enhance the scalability and impact of dMH interventions.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".