Designing Culturally Adapted Digital Mental Health Support Tool for Chinese-Speaking International Students in Australia: A Qualitative Co-design Study
Bibliographic record
Abstract
Background: International students, especially Chinese-speaking students, who grew up with collectivist values and the linguistic and cultural characteristics face a higher risk of mental health issues due to the challenges of geographical, linguistic, and cultural transitions when studying abroad. While digital technology has shown promise in supporting mental health, few studies have focused on designing tools specifically for Chinese-speaking international students and the challenges they face. Objective: This study aimed to design and develop a self-directed digital mental health support tool that provides culturally safe and appropriate support for international students. Methods: A co-design approach was used across 2 study phases, Phase 1 (interviews) and Phase 2 (co-design workshops), to explore the design implications of the digital tool. Inductive thematic analysis was conducted to extract design insights and considerations. Results: Findings show that participants faced a wide range of challenges arising from cross-cultural, academic, and daily life demands, which further contributed to increased levels of stress and negative feelings. These findings highlight a need to improve mental health awareness, literacy, and help-seeking intentions in this vulnerable group. These insights informed the design that emphasized the integration of culturally adapted resources with self-directed learning tools. Conclusions: Based on the findings, a personalized, self-directed, and culturally adapted design has been proposed that creates a bridging pathway linking students' immediate challenges with mental health education and support. This design offers a clear set of implications for enhancing international students' mental health awareness, literacy, and help-seeking behaviors, thereby providing essential support for this population.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".