Co-designing Mental Health Technologies with International University Students in Canada
Bibliographic record
Abstract
Mental health problems are a serious concern among university students, and international students in Canada are known to be particularly vulnerable due to the underutilization of mental health services and unfamiliarity with Western approaches to mental health. However, international students' mental well-being remains underexplored in HCI. In this study, we conducted remote synchronous remote co-design sessions with 19 participants (14 international students, 5 mental health professionals) to understand concretely what types of designs for interactive technologies suit these students' mental health needs and challenges. Based on their brainstormed ideas and sketches, we produced a set of design dimensions that span different types of support, interaction, and safety. The dimensions were then used to develop a set of four medium-fidelity mockups that spanned these dimensions, presenting a diverse range of design features. Using these mockups, we elicited feedback in an online survey from the same participants. Findings suggest that the students negotiate a complex understanding of helpfulness, comfort, and trust when they consider what types of designs to consider using. Each mockup highlights different ways to support individual differences and preferences. Our work serves as a foundation for designing technologies that can ease issues with accessibility and be more inclusive of international students' cultural backgrounds.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".