Inclusion of Individuals With Lived Experiences in the Development of a Digital Intervention for Co-Occurring Depression and Cannabis Use: Mixed Methods Investigation
Notice bibliographique
Résumé
BACKGROUND: Existing interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depression and cannabis use from receiving adequate treatment. Digital interventions can increase the scalability and accessibility for these individuals, but few digital interventions exist to treat both disorders simultaneously. Targeting transdiagnostic processes of these disorders with a digital intervention-specifically positive valence system dysfunction-may yield improved access and outcomes. OBJECTIVE: Recent research has highlighted a need for the inclusion of individuals with lived experiences to assist in the co-design of interventions to enhance scalability and relevance of an intervention. Thus, the purpose of this study is to describe the process of eliciting feedback from individuals with elevated depressed symptoms and cannabis use and co-designing a digital intervention, Amplification of Positivity-Cannabis Use Disorder (AMP-C), focused on improving positive valence system dysfunction in these disorders. METHODS: Ten individuals who endorsed moderate to severe depressive symptoms and regular cannabis use (2-3×/week) were recruited online via Meta ads. Using a mixed methods approach, participants completed a 1-hour mixed methods interview over Zoom (Zoom Technologies Inc) where they gave their feedback and suggestions for the development of a mental health app, based on an existing treatment targeting positive valence system dysfunction, for depressive symptoms and cannabis use. The qualitative approach allowed for a broader investigation of participants' wants and needs regarding the engagement and scalability of AMP-C, and the quantitative approach allowed for specific ratings of intervention components to be potentially included. RESULTS: Participants perceived the 13 different components of AMP-C as overall helpful (mean 3.9-4.4, SD 0.5-1.1) and interesting (mean 4.0-4.9, SD 0.3-1.1) on a scale from 1 (not at all) to 5 (extremely). They gave qualitative feedback for increasing engagement in the app, including adding a social component, using notifications, and being able to track their symptoms and progress over time. CONCLUSIONS: This study highlights the importance of including individuals with lived experiences in the development of interventions, including digital interventions. This inclusion resulted in valuable feedback and suggestions for improving the proposed digital intervention targeting the positive valence system, AMP-C, to better match the wants and needs of individuals with depressive symptoms and cannabis use.
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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 | 0,040 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».