Examining the Feasibility of Implementing Digital Mental Health Innovations Into Hospitals to Support Youth in Suicide Crisis: Interview Study With Young People and Health Professionals
Notice bibliographique
Résumé
BACKGROUND: Hospitals are insufficiently resourced to appropriately support young people who present with suicidal crises. Digital mental health innovations have the potential to provide cost-effective models of care to address this service gap and improve care experiences for young people. However, little is currently known about whether digital innovations are feasible to integrate into complex hospital settings or how they should be introduced for sustainability. OBJECTIVE: This qualitative study explored the potential benefits, barriers, and collective action required for integrating digital therapeutics for the management of suicidal distress in youth into routine hospital practice. Addressing these knowledge gaps is a critical first step in designing digital innovations and implementation strategies that enable uptake and integration. METHODS: We conducted a series of semistructured interviews with young people who had presented to an Australian hospital for a suicide crisis in the previous 12 months and hospital staff who interacted with these young people. Participants were recruited from the community nationally via social media advertisements on the web. Interviews were conducted individually, and participants were reimbursed for their time. Using the Normalization Process Theory framework, we developed an interview guide to clarify the processes and conditions that influence whether and how an innovation becomes part of routine practice in complex health systems. RESULTS: Analysis of 29 interviews (n=17, 59% young people and n=12, 41% hospital staff) yielded 4 themes that were mapped onto 3 Normalization Process Theory constructs related to coherence building, cognitive participation, and collective action. Overall, digital innovations were seen as a beneficial complement to but not a substitute for in-person clinical services. The timing of delivery was important, with the agreement that digital therapeutics could be provided to patients while they were waiting to be assessed or shortly before discharge. Staff training to increase digital literacy was considered key to implementation, but there were mixed views on the level of staff assistance needed to support young people in engaging with digital innovations. Improving access to technological devices and internet connectivity, increasing staff motivation to facilitate the use of the digital therapeutic, and allowing patients autonomy over the use of the digital therapeutic were identified as other factors critical to integration. CONCLUSIONS: Integrating digital innovations into current models of patient care for young people presenting to hospital in acute suicide crises is challenging because of several existing resource, logistical, and technical barriers. Scoping the appropriateness of new innovations with relevant key stakeholders as early as possible in the development process should be prioritized as the best opportunity to preemptively identify and address barriers to implementation.
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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 | 0,013 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».