A Human-Centered Approach for a Student Mental Health and Well-Being Mobile App: Protocol for Development, Implementation, and Evaluation
Notice bibliographique
Résumé
BACKGROUND: The rising prevalence of mental health concerns among students is prompting universities to explore innovative solutions to support student well-being. This paper describes the protocol for the development, implementation, and evaluation of a mobile app designed to address the mental health and wellness needs of students. This project employs a student-centered approach, partnering with students from the initial needs analysis through to the final design and implementation stages. OBJECTIVE: The app aims to increase the use of campus resources that address student mental health and wellness by improving the awareness of these resources through user-designated preferences that are established on the initial use of the app and then iteratively refined as it is used. The app is linked to the campus student's electronic health record so that health and wellness services can be coordinated and enhanced and the student journey to and through care become more seamless. The long-term objective is to leverage data from both the app and electronic health record to improve individual and population health for the entire campus. METHODS: At the beginning of the project, a comprehensive logic model was created to outline the core inputs, activities, outputs, outcomes, and long-term impacts that were desired for the app. The model emphasized the integration of the app within existing campus mental health and wellness services and its potential to foster a culture of well-being across the university community. An evaluation plan was developed that incorporates both quantitative and qualitative methods through biannual assessments to track trends and app impact across campus in addition to feasibility, acceptability, and usability as well as its reach, effectiveness, and sustainability. Validated measures such as the Patient Health Questionnaire and Generalized Anxiety Disorder scale were selected to track changes in mental health and wellness, while custom surveys and analytics will gauge user engagement and satisfaction. New students, including freshmen, transfers, and first-year medical students, are invited to participate after giving informed consent. They receive compensation for their involvement in both quantitative and qualitative assessments. RESULTS: As of March 2025, we have collected over 600 survey responses from freshmen, transfer, and medical students. A second survey round and additional focus groups are planned for April to May 2025. No analyses have been conducted yet. The findings from this project have the potential to inform similar efforts at other institutions and contribute to the broader field of digital mental health innovation and the development of well-being interventions tailored for young people. CONCLUSIONS: By leveraging digital technology and actively engaging students in supporting their well-being, this initiative represents an innovative user-centered approach to improve mental health and wellness support on university campuses. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/68368.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,176 | 0,157 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,054 | 0,017 |
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 source (Gemma direct ou Codex distillé), 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 ».