Evaluating the Effectiveness of a Codeveloped e-Mental Health Intervention for University Students: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: University life typically occurs during a period of life transition, where the incidence of mental health and substance use problems and disorders peaks. However, relatively few students obtain effective treatment and support. e-Interventions have proven effective in improving the psychological outcomes of university students and have the potential to provide scalable services that can easily integrate into existing models of care. Minder is a mobile app codeveloped with university students that offers users a collection of evidence-based interventions tailored to help university students maintain their mental health and well-being and manage their substance use. OBJECTIVE: This paper describes the protocol for a randomized controlled trial (RCT) that aims to assess the effectiveness of the Minder app in improving the mental health and substance use outcomes of university students. METHODS: This study is a 2-arm, parallel assignment, single-blinded, 30-day RCT with 1 intervention group and 1 waitlist control group. Overall, 1496 (748 per trial arm) university students from the University of British Columbia Vancouver Campus (N=54,000) who are aged ≥17 years, have a smartphone with Wi-Fi or cellular data, and speak English will be recruited via a variety of web-based and offline strategies. Participants will be randomized into the intervention or control group after completing a baseline survey. Those randomized into the intervention group will gain immediate access to the Minder app and will be assessed at 2 weeks and 30 days. Those randomized into the control group will be given access to the app content after their follow-up assessment at 30 days. The primary outcomes are measured from baseline to follow-up at 30 days and include changes in general anxiety symptomology, depressive symptomology, and alcohol consumption risk measured by the General Anxiety Disorder 7-Item scale, Patient Health Questionnaire 9-Item scale, and US Alcohol Use Disorders Identification Test-Consumption Scale, respectively. Secondary outcomes include measures related to changes in the frequency of substance use, mental well-being, self-efficacy in managing mental health and substance use, readiness to change, and self-reported use of mental health services and supports (including referral) from baseline to follow-up at 30 days. RESULTS: Trial recruitment and data collection began in September 2022, and the completion of data collection for the trial is anticipated by June 2023. As of May 10, 2023, a total of 1425 participants have been enrolled. CONCLUSIONS: The RCT described in this protocol paper will assess whether the Minder app is effective in improving the mental health and substance use outcomes of a general population of Canadian university students. Additional secondary outcome research aims to explore additional outcomes of interest for further research and better understand how to support students' general mental well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT05606601; https://clinicaltrials.gov/ct2/show/NCT05606601. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49364.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,065 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,009 | 0,006 |
| Méta-épidémiologie (sens large) | 0,018 | 0,010 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,012 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,103 | 0,020 |
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 ».