Development of a Peer Support Mobile App and Web-Based Lesson for Adolescent Mental Health (Mind Your Mate): User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Digital technologies and mobile interventions are possible tools for prevention initiatives to target the substantial social and economic impacts that anxiety, mood, and substance use disorders have on young people. OBJECTIVE: This paper described the design and development of the Mind your Mate program, a smartphone app and introductory classroom lesson enhancing peer support around the topics of anxiety, depression, and substance use for adolescents. METHODS: The development of Mind your Mate was an iterative process conducted in collaboration with adolescents (n=23), experts, school staff, and software developers. The development process consisted of 3 stages: scoping; end-user consultations, including a web-based survey and 2 focus groups with 23 adolescents (mean age 15.9, SD 0.6 years); and app development and beta-testing. RESULTS: This process resulted in a smartphone peer support app and introductory classroom lesson aimed at empowering adolescents to access evidence-based information and tools to better support peers regarding mental health and substance use-related issues. The program contains links to external support services and encourages adolescents to reach out for help if they are concerned about themselves or a friend. CONCLUSIONS: The Mind your Mate program was developed in collaboration with a number of key stakeholders in youth mental health, including adolescents. The resulting program has the potential to be taken to scale to aid prevention efforts for youth mental health and substance use. The next step is to conduct a randomized controlled trial testing the feasibility, acceptability, and efficacy of the program.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".