Exploring Youth Perspectives on Digital Mental Health Platforms: Qualitative Descriptive Study
Notice bibliographique
Résumé
Background: The increasing prevalence of mental health disorders among youth underscores the need for accessible and effective interventions. Digital mental health (dMH) platforms like Innowell offer promising solutions by increasing access to mental health care for young people. Innowell is a web-based platform that supports youth mental health by providing personalized measurement-based care in collaboration with a youth's health care providers. However, understanding youth perspectives on these platforms is crucial for ensuring successful implementation and sustained engagement. Objective: This study aimed to explore youth perspectives on the implementation of the Innowell platform, identifying key factors influencing uptake, engagement, and long-term retention. Methods: A qualitative descriptive approach was used to examine youth perspectives. Data were collected through 9 focus groups and 1 interview, involving 39 participants aged 15-24 years from urban (23/39, 59%) and rural (16/39, 41%) communities in Alberta, Canada. Participants were recruited through mental health clinics and community organizations. Thematic analysis was conducted on the transcripts to identify factors that support or hinder engagement with the platform. Results: Participants emphasized the importance of privacy, security, and personalization in building trust in the platform, with 72% (28/39) reporting that clear communication about data protection would increase their likelihood of use. Progress tracking features, such as symptom trend visualizations and diaries, were identified by 65% (25/39) of participants as critical for sustaining engagement. Ease of use was highlighted, with 58% (23/39) preferring mobile app functionality over web-based interfaces. Dynamic content and personalized notifications were suggested as strategies to maintain long-term use, with 64% (25/39) of participants valuing customizable reminders to encourage daily interactions. Rural participants (16/39, 41%) noted the need for offline functionality due to inconsistent internet access. In addition, participants recommended features such as crisis support, professional communication channels, and access to local mental health resources. Conclusions: Youth-centered design is essential for enhancing the usability and engagement of dMH platforms like Innowell. Key features prioritized by participants included privacy, security, progress tracking, and personalization. Dynamic and user-friendly interfaces, along with the ability to customize notifications and access professional support, were critical for fostering long-term engagement. Insights from this study provide actionable recommendations for optimizing dMH platforms to meet the mental health needs of young people, particularly in diverse urban and rural settings. Future research should explore implementation strategies tailored to specific user demographics to enhance the scalability and impact of dMH interventions.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».