Exploring the Acceptability, Appropriateness, and Utility of a Digital Single-Session Intervention (Project SOLVE-NZ) for Adolescent Mental Health in New Zealand: Interview Study Among Students and Teachers
Notice bibliographique
Résumé
BACKGROUND: Globally, we face a significant treatment gap in mental health care, with extensive wait times, exorbitant prices, and concerns about appropriateness for non-Western clients. Digital single-session interventions (SSIs) may offer a promising alternative. SSIs target particular mechanisms that underlie broad-ranging psychopathology, including deficits in problem-solving skills. OBJECTIVE: Developed in the United States, Project SOLVE is a digital SSI that teaches problem-solving skills to adolescents. This study evaluated the acceptability, appropriateness, and utility of an adapted version, Project SOLVE-NZ, among rangatahi (young people) in Aotearoa New Zealand. Additionally, we evaluated a comparable online activity, Project Success-NZ, as a potential active control condition in a future randomized controlled trial of Project SOLVE-NZ. METHODS: A sample of school students and teachers completed Project SOLVE-NZ and Project Success-NZ. Feedback on the interventions was collected through focus groups and semistructured interviews. Interviews were recorded, transcribed, and analyzed using reflexive thematic analysis. RESULTS: In total, 12 students (aged between 13 and 14 years; female students: n=6, 50%) participated in a focus group, and 8 teachers (teaching experience: mean 8.75, SD 7.96 years; female teachers: n=5, 62.5%) participated in individual interviews. Participants endorsed the sociocultural relevance of Project SOLVE-NZ and Project Success-NZ to rangatahi in Aotearoa New Zealand and viewed all existing adaptations favorably. Participants felt that the interventions would be valuable to a wide range of rangatahi, helping to fill gaps in students' learning and providing benefits to mental health. Participants also believed that the interventions may be particularly relevant for youths experiencing economic hardship. Interestingly, most participants had no preference for either Project SOLVE-NZ or Project Success-NZ, and they believed that both interventions could provide ongoing support to rangatahi throughout the school year. Teachers provided some suggestions on increasing student engagement with the interventions, namely, through increased cultural and gender representation, visual and literacy aids, whakawhanaungatanga (relationship building), and teacher guidance. Overall, interviews revealed that both interventions were perceived as acceptable, appropriate, and useful for rangatahi in New Zealand and highlighted further adaptations that could be made prior to a randomized controlled trial of Project SOLVE-NZ across schools nationwide. CONCLUSIONS: Digital SSIs show promise in addressing the mental health treatment gap for adolescents. Both Project SOLVE-NZ and Project Success-NZ were well-received by students and teachers in Aotearoa New Zealand and may provide benefits to youth mental health. We make the following recommendations for others interested in designing digital SSIs or similar tools for young people: involve rangatahi and relevant stakeholders in the design process, consider how the intervention will be implemented, ensure that the intervention accommodates a range of cognitive abilities, and ensure that the intervention reflects the diversity of rangatahi today.
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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 | 0,009 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».