Personalized Help-Seeking Web Application for Chinese-Speaking International University Students: Development and Usability Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».