Ownership, Use of, and Interest in Digital Mental Health Technologies Among Clinicians and Young People Across a Spectrum of Clinical Care Needs: Cross-sectional Survey
Notice bibliographique
Résumé
Background There is currently an increased interest in and acceptance of technology-enabled mental health care. To adequately harness this opportunity, it is critical that the design and development of digital mental health technologies be informed by the needs and preferences of end users. Despite young people and clinicians being the predominant users of such technologies, few studies have examined their perspectives on different digital mental health technologies. Objective This study aims to understand the technologies that young people have access to and use in their everyday lives and what applications of these technologies they are interested in to support their mental health. The study also explores the technologies that youth mental health clinicians currently use within their practice and what applications of these technologies they are interested in to support their clients’ mental health. Methods Youth mental health service users (aged 12-25 years) from both primary and specialist services, young people from the general population (aged 16-25 years), and youth mental health clinicians completed a web-based survey exploring technology ownership, use of, and interest levels in using different digital interventions to support their mental health or that of their clients. Results A total of 588 young people and 73 youth mental health clinicians completed the survey. Smartphone ownership or private access among young people within mental health services and the general population was universal (611/617, 99%), with high levels of access to computers and social media. Youth technology use was frequent, with 63.3% (387/611) using smartphones several times an hour. Clinicians reported using smartphones (61/76, 80%) and video chat (69/76, 91%) commonly in clinical practice and found them to be helpful. Approximately 50% (296/609) of the young people used mental health apps, which was significantly less than the clinicians (χ23=28.8, n=670; P<.001). Similarly, clinicians were significantly more interested in using technology for mental health support than young people (H3=55.90; P<.001), with 100% (73/73) of clinicians being at least slightly interested in technology to support mental health compared with 88% (520/591) of young people. Follow-up tests revealed no difference in interest between young people from the general population, primary mental health services, and specialist mental health services (all P>.23). Young people were most interested in web-based self-help, mobile self-help, and blended therapy. Conclusions Technology access is pervasive among young people within and outside of youth mental health services; clinicians are already using technology to support clinical care, and there is widespread interest in digital mental health technologies among these groups of end users. These findings provide important insights into the perspectives of young people and clinicians regarding the value of digital mental health interventions in supporting youth mental health.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».