Personalized Help-Seeking Web Application for Chinese-Speaking International University Students: Development and Usability Study
Bibliographic record
Abstract
Background The mental health of international students is a growing concern for education providers, students, and their families. Chinese international students have low rates of help seeking owing to language, stigma, and mental health literacy barriers. Web-based help-seeking interventions may improve the rate of help seeking among Chinese international students. Objective This study aimed to describe the development of a mental well-being web app providing personalized feedback and tailored psychoeducation and resources to support help seeking among international university students whose first language is Chinese and test the web application’s uptake and engagement. Methods The bilingual MindYourHead web application contains 6 in-app assessments for various areas of mental health, and users are provided with personalized feedback on symptom severity, psychoeducation tailored to the person’s symptoms and information about relevant interventions, and tailored links to external resources and mental health services. A feasibility study was conducted within a school at the University of Sydney to examine the uptake and engagement of the web application among Chinese international students and any demographic characteristics or help-seeking attitudes or intentions that were associated with its engagement. Results A total of 130 Chinese international students signed up on the web application. There was an uptake of 13.4% (122/908) in the schools’ Chinese student enrollment. Most participants (76/130, 58.5%) preferred to use the web application in Chinese and used informal but not formal help for their mental health. There was considerable attrition owing to a design issue, and only 46 students gained access to the full content of the web application. Of these, 67% (31/46) of participants completed 1 or more of the in-app mental well-being assessments. The most commonly engaged in-app assessments were distress (23/31, 74%), stress (17/31, 55%), and sleep (15/31, 48%), with the majority scoring within the moderate- or high-risk level of the score range. In total, 10% (9/81) of the completed in-app assessments led to clicks to external resources or services. No demographic or help-seeking intentions or attitudes were associated with web-application engagement. Conclusions There were promising levels of demand, uptake, and engagement with the MindYourHead web application. The web application appears to attract students who wished to access mental health information in their native language, those who had poor mental health in the past but relied on informal support, and those who were at moderate or high risk of poor mental well-being. Further research is required to explore ways to improve uptake and engagement and to test the efficacy of the web application on Chinese international students’ mental health literacy, stigma, and help seeking.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".