Co-developing tools to support student mental health and substance use: Minder app development from conceptualization to realization
Bibliographic record
Abstract
University students experience a high prevalence of mental health and substance use concerns; however, few students access support for these challenges. Although digital mental health interventions have been promoted as a means of addressing this need, engagement with these tools is often poor. A lack of user-centric design is frequently cited as a reason for low engagement. The goal of this study is to describe the co-development processes and associated feedback used to develop the Minder app, a tool designed to support a non-clinical population of university students to maintain mental wellbeing and manage substance use. This process can be organized into three main phases: conceptualization and initial app design, iterative user testing, and final app design. As a result of meaningful engagement with end-users throughout the design and testing process, key changes were made to the design (e.g., graphical interface), content (e.g., language used, addition of components related to general wellbeing), and support (e.g., peer coaching) provided within the app. In addition to describing these changes, we also discuss considerations related to the broader implementation and scale-up of the Minder app within existing university systems.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".